CONTEXT INJECTION FOR IMPROVED AI RESPONSE

A method comprises: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an AI model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

TECHNICAL FIELD

The present disclosure relates to improving artificial intelligence (AI) responses to a user query.

BACKGROUND

In the artificial intelligence (AI) natural language processing (NLP) space, prompt engineering is a fairly new area of research. One way to define it is that the description of a task that the AI NLP is supposed to accomplish is embedded in a language input, e.g., as a question, instead of being implicitly given. Prompt engineering typically works by converting one or more descriptions of the task to a prompt-based dataset and training a language model with what has been called “prompt-based learning” or just “prompt learning.”

Providing the proper context for user queries to an AI model can greatly increase the relevance and clarity of an AI response from the AI model. Currently, this process is performed consciously by the user, which is time-consuming and tedious.

DETAILED DESCRIPTION

Overview

In an embodiment, a method comprises: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an artificial intelligence (AI) model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

Example Embodiments

FIG.1is an example network environment100in which embodiments directed to context injection for an improved artificial intelligence (AI) response may be implemented. The embodiments advantageously perform the context injection in an automated and real-time manner, which is transparent to a user. Network environment100includes a computer workstation102(also referred to as a “controller”) operated by a user, a database104(or database application, user profile, and the like) that stores user attributes106associated with the user and that are indicative of a skill set and a knowledge level of the user on particular topics, and an AI model108(e.g., a generative AI model, such as an AI Chat Bot) connected to, and able to communicate with each other, over a network110. Computer workstation102hosts a context injection application114that interacts with user attributes106and AI model108to implement embodiments presented herein.

At a high-level, starting with an initial query on a topic entered by the user, context injection application114iteratively adds topically/contextually-relevant user attributes106to the initial query, to generate iterative queries that are increasingly user-context aware (and query-topic aware), and provides the same to AI model108. In response, AI model108iteratively generates increasingly more topically-relevant and technically-detailed AI responses. The foregoing iterations are transparent to the user. Context injection application114determines a final AI response among the AI responses that best or most closely matches the skill set and the knowledge level of the user, and presents the final AI response to the user.

FIG.2is an illustration of an example method200in which additional user context is added to an initial query from the user in order to provide an AI response to the query that aligns with a skill set and a knowledge level of the user. Attributes associated with the user (i.e., user attributes) or associated with the topic of interest expressed in the query can be systematically and iteratively injected into the initial query to generate/solicit iteratively better AI responses (i.e., answers to the queries), which are evaluated using existing comprehension scoring algorithms to eventually deliver a response that would be the “best” answer based on the attributes.

According to method200, a user generates an initial query202and provides the same to context assessment204. Context assessment204interfaces with user attributes106associated with the user. The user attributes106include a location208of the user, a skill level210of the user, and topic information212indicative of a topic complexity for topics with which the user is associated. Context assessment204injects into initial query202additional user-related context, including keywords, retrieved from user attributes106, to produce a new query218. New query218represents an expanded version of initial query202. Context assessment204provides new query218to AI model108. AI model108generates an AI response222or answer responsive to new query218, and provides the AI response to response evaluation224.

Response evaluation224determines a composite reading score or “comprehension level” of AI response222. Response evaluation224compares AI response222to user attributes106, to produce the comprehension level as a measure of similarly between the AI response and the user attributes (i.e., the skill set and knowledge level of the user). The higher the comprehension level, the higher the level of similarity. Response evaluation224provides the comprehension level to comprehension improvement test226. Comprehension improvement test226determines whether the comprehension level improved relative to a previously computed comprehension level from a previous iteration of the process, and also determines whether the comprehension level can be improved further. When the comprehension level shows little or no improvement, and/or the comprehension level cannot be improved further, at230, AI response222is delivered/presented to the user as a best and final response.

On the other hand, when the comprehension improved, or when the comprehension level can be improved/increased further, the above described process repeats using/treating new query218as an initial query. More specifically, flow proceeds to context adjustment232(which may form part of context assessment204). Context adjustment232retrieves additional user attributes that are contextually relevant of/from user attributes106, adds the additional user attributes to new query218to produce a further expanded query, provides the same to AI model108, and the process repeats.

FIG.3is an illustration of an example flow300of prompt engineering and context injection that expands on method200ofFIG.2. At a high level, a query interface collects a question from a user, generates additional queries and AI responses on behalf of the user, and eventually returns an AI response that is most in-line with the skill set and the knowledge level (including a reading level) of the user, as described below.

At A, the user constructs a first query304a(i.e., an initial query) and posts the same to a query interface/context introducer306. Query interface/context introducer306relays to AI model108first query304a, without additional context. AI model108may be an AI Chat Bot that is designated for the workflow. In response, AI model108generates a first AI response304band, at B, returns the same to response analysis and selection312through query interface/context introducer306. At C, response analysis and selection312determines whether to perform an iteration to expand first query304a. To make this determination, response analysis and selection312:a. Determines/evaluates an AI response comprehension level of first AI response304b, which represents a level of comprehension a user should have to understand the AI response. Any known or hereafter developed technique, including a machine learning (ML)/AI-based technique may be used to evaluate the response comprehension level on the topic. Evaluating the response comprehension level may be based on a reading comprehension level of the response and a quantity of specific details (e.g., technical terms) in the response; in which case the response comprehension level increases as the reading comprehension level and/or the quantity of technical terms increase. In this case, the AI response comprehension level of first AI response304b=5.b. Infers a user comprehension level of the user based on user attributes106that are relevant to the topic of interest (e.g., relevant to the topic/context of first query304(a)). In this case, the user comprehension level=18. Any known or hereafter developed technique, including an ML/AI-based technique may be used to infer the user comprehension level on the topic based on the user attributes. In another example, the user comprehension level on the topic may be a predetermined level that is stored in the user attributes.c. Determines a level of match or similarly between the comprehension levels from (a) and (b). Operations (a), (b), and (c) may be achieved using known comprehension scoring techniques. For example, the Flesch-Kincaid readability test can determine a reading ease or difficulty of a document. An ensemble of such methods can be compared against either user specified declarations (e.g., “I am a PhD in my field of expertise” or “I am a novice”), or learned through behavioral profiling of the individual-if an individual authors complicated documentation on a subject, the individual probably has a more advanced knowledge and understanding of the subject. To summarize, such topic-based comprehension assessment can be learned, as well as self-declared and then compared to response text using known comprehension capabilities.d. Determines whether the AI response comprehension level has increased (i.e., improved) relative to a previous iteration or an initial level (e.g., initially set=0).

In this case, based on operations (a)-(d), response analysis and selection312learns that the AI response comprehension level has improved to 5 (from 0 initially) but is well below the user comprehension level=18, which means there is room for improvement in the AI response. Therefore, provided that topically-relevant user attributes have not been exhausted by previous iterations, flow proceeds to a next/subsequent iteration, described below. If the user attributes have been exhausted, the iterations end.

At D, in the next/subsequent iteration, query interface/context introducer306adds to first query304amore contextually-relevant user attributes (e.g., additional user attributes relevant to the topic of first query304a). For example, query interface/context introducer306adds to first query304auser location and other attributes of user attributes106, to produce a second query314athat is more user-context aware than first query304a, and sends the same to AI model108. In response, AI model108generates a second AI response314band provides the same back to response analysis and selection312. To determine whether to perform a next iteration, response analysis and selection312repeats operations (a)-(d) described above. Through operations (a)-(d), response analysis and selection312learns that second AI response314bhas an AI response comprehension level=6, which represents an improvement from the last iteration, but there is still room for improvement given that the user comprehension level=18. Therefore, flow proceeds to a next iteration.

In a next iteration, query interface/context introducer306adds to second query314amore contextually-relevant user attributes and other information retrieved from user attributes106, to produce a third query320athat is more user-context aware than second query314a, and sends the same to AI model108. In response, AI model108generates a third AI response320band provides the same back to response analysis and selection312. Response analysis and selection312determines whether to perform a next iteration. Repeating operations (a)-(d) above, response analysis and selection312learns that third AI response320bhas an AI response comprehension level=18, which represents an improvement from the last iteration, but that is now well matched to the user comprehension level=18. Therefore, there is no more room for improvement and another iteration to expand the query further is unnecessary. Response analysis and selection312selects third AI response320b(which is the last and final AI response) as a final AI response that most closely matches the relevant user attributes.

The above described process is summarized as follows. An initial query is asked by the user and posted against the query interface/context introducer306. The query interface relays the question to an AI model (108), such as a designated AI Chat Bot, which generates an AI response. The AI response is analyzed to produce a reading comprehension level associated with the AI response. The query interface iteratively adds additional information about the user to further define the relationship that the user may have with a given topic. Natural Language models can be used to introduce additional keywords and context in the form of a new question. The multiple responses are returned from the designated Chat Bot to response analysis and selection (312), which analyzes the results of each response and gauges similarity of keywords associated with the skillset of the user as well as an inferred comprehension level for the given topic. The response that most closely aligns with the skill set and comprehension level of the user is returned as the accepted answer.

Advantageous features of the above-described method include: a query interface/context introducer306that processes initial questions of a user and formulates additional expanded questions based on the attributes of the user; response analysis and selection312configured to receive multiple responses from a Chat Bot and analyze keyword, attribute, and reading level comprehension for alignment with a Chat Bot user; collecting user attributes and topic maturity for the purpose of prompt engineering; gradually injecting user context to an AI model query with direct relation to topic comprehension; iteratively applying “what-if” scenarios of alternative questions based on collected attributes of a user and topic maturity; and continually evaluating topic comprehension compared to the knowledge level and skill set of a model querier; and identifying targeted answers from AI models based on comprehension evaluation.

FIG.4is an illustration of example context injection400expanding on iterative context injection D fromFIG.3. Query interface/context introducer306supplies a query (e.g., an initial query or a next iteration query) to attribute-to-topic mapping402. Attribute-to-topic mapping402performs topic modeling (which may include ML/AI modeling) and attribute alignment between the topic and the query, to map the best-fitting user attributes to the topic of the query. For example, attribute-to-topic mapping402searches a user profile408(which may include user attributes106) based on the query to find user attributes that contextually match a topic expressed in the query. User profile408may include information that conveys technical knowledge and skill sets of the user. User profile408may include a corporate directory that includes information about the user, a list of publications of the user, web postings by the user, club and society memberships of the user, and so on. Any known or hereafter developed attribute-to-topic mapping technique may be used, such as simple keyword matching or more sophisticated matching techniques. Role is one aspect that could help provide context, but other classifications extend beyond role.

Attribute-to-topic mapping evaluates the user comprehension level on topics and skills to identify additional user attributes and passes the same to query injection404. Query injection404adds the additional user attributes to the query to produce an expanded query. Some roles and skills have little or no relevance to a prompt (query), while others are highly relevant. For example, when the query is directed to network technology, such as the Internet, and so on, attribute-to-topic mapping402may return from user profile408user attributes such as the border gateway protocol (BGP), Cisco expert level certification (CCIE), location San Jose, and so on, but not Ford F-150 Owner.

More generally, metadata can be collected on the user and generated from many sources and existing systems. For example, attributes such as skills and location may be specified by the user themselves in corporate directories. Additionally, the behavior and usage patterns of a user could be profiled to align with certain topics, for example, if the user regularly posts on a forum designed for a specific interest or responds to emails pertaining to a particular subject as a subject matter expert. Keywords can be extracted from each of these profiles, and used with techniques such as Named Entity Recognition to determine if frequently occurring keywords and topics can be integrated into a prompt and still obey rules of grammar.

FIGS.5and6described below are comparative examples that show how simply making it known that a user is a Webex administrator or not can dramatically shift AI responses to a query.FIG.5shows an example initial query502that indicates that a user is a Webex user experiencing a single sign-on (SSO) login problem, and an initial AI response504to the initial query.

FIG.6shows an example second query602with additional user context to indicate that the user is a Webex administrator, and a second AI response604to the second query. The additional user context is injected into initial query502using techniques presented herein. The second AI response604is substantially more technically detailed and useful to the user.

In summary, when a user submits a query on a topic to an AI model to solicit an AI response to the query, a method iteratively adds topic-relevant attributes associated with the user (e.g., location of the user, technical expertise of the user, and so on) to the query to generate iterative queries of increasing complexity for the AI model, which in turn solicit, from the AI model, iterative AI responses of increasing complexity, until the AI model produces a final iterative AI response that has a level of difficulty to understand that most nearly matches a level of comprehension of the user (that is determined based on the user attributes).

An example method comprises:a. Receiving a query from a user who is associated with user attributes indicative of a knowledge and skill level, and thus user comprehension level, of the user on a technical topic expressed in the query, and providing the query to an AI model.b. In response to the query, receiving from the AI model an AI response that has a response comprehension level related to the technical topic.c. Iteratively adding technical topic-relevant user attributes to the query, to produce iterated queries of increasing technical complexity/technical detail (i.e., increasingly more specific), and providing the iterated queries to the AI model.d. Receiving from the AI model iterated AI responses that are increasingly more technically complex (and increasingly more specific) and that have increasingly higher response comprehension levels related to the technical topic in correspondence with the iterated queries.e. Determining, among the AI responses, a final AI response having a response comprehension level that most nearly matches the user comprehension level.

FIG.7is a flowchart of an example iterative method700of improving generative AI responses to queries on a topic (e.g., a technical topic such as network engineering). Operations of iterative method700are described above. Iterative method700may be performed by a workstation/controller operated by a user.

At702, the controller receives a query on the topic from the user. The user is associated with user attributes stored in a database and that are indicative of a user comprehension level on the topic, including a skill set and a knowledge level of the user on the topic. The controller may evaluate the user comprehension level based on the user attributes, or the user comprehension level may be predetermined. The controller provides the query to an AI model, such as a generative AI model trained to generate responses to queries on the topic.

At704, the controller receives from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level.

The controller performs next operations706-712iteratively/repeatedly, one query-response at a time. Operations706-712represent an iterative process.

At706, the controller iteratively maps the user attributes to the topic to identify topically-relevant user attributes (i.e., particular user attributes) among the user attributes that match or most closely align to the topic expressed in the query. The controller performs such mapping prior to/for each iterative adding operation, described below. The mapping may include searching the user attributes in a user profile of the user that includes information that conveys technical knowledge and skill sets of the user related to the topic. For example, the user profile may include a corporate directory that includes location and company role information about the user, a list of technical publications on the topic authored by the user, and Internet postings on the topic authored by the user.

At708, the controller iteratively adds, to the query, the topically-relevant user attributes indicated by the iterative mapping to produce iterative queries that incrementally increase in technical detail on the topic. The controller provides the iterative queries to the AI model.

At710, responsive to providing the iterative queries, the controller receives, from the AI model, iterative responses (e.g., one iterative response per iterative query) that incrementally increase in technical detail on the topic and have response comprehension levels that increase on the topic. The controller evaluates the response comprehension levels of the iterative responses based on a combination of (i) reading comprehension levels of the iterative responses, and (ii) quantities of technical details included in the iterative responses, for example. In that example, the controller increases the response comprehension levels with increases in the reading comprehension levels, and with increases in the quantities of the technical details.

At712, upon determining that the response comprehension levels are less than the user comprehension level and that the topically-relevant user attributes have not been exhausted by previous iterations, the controller continues iterating to expand the iterative queries.

At714, upon determining that the response comprehension levels are not less than the user comprehension level, or that the topically-relevant user attributes have been exhausted, the controller stops iterating, and identifies as a final response a last iterative response that has a comprehension level that most nearly matches the user comprehension level.

In summary, in an embodiment, a method includes receiving a query on a technical topic from a user who is associated with user attributes indicative of a skill set and knowledge (and comprehension level) of the user, and providing the query to an AI model to solicit from the AI model an AI response (i.e., an AI answer) on the technical topic and that has a quantifiable response comprehension level on the technical topic. The method systematically and iteratively injects into the query additional user attributes that are related/matched to the technical topic (i.e., topically-related user attributes) to generate iteratively expanded queries of increasing complexity on the technical topic in order to solicit iterative AI responses of increasing complexity on the technical topic, and which are iteratively/increasingly more aligned with the skill set and knowledge (and the comprehension level) of the user on the technical topic. The method evaluates comprehension levels of the AI responses using comprehension scoring algorithms to eventually deliver a final AI response that is a nearest match to the technical topic and the skill set and knowledge (and the comprehension level) of the user, as indicated by the user attributes.

Referring toFIG.8,FIG.8illustrates a hardware block diagram of a computing device800that may perform functions associated with operations discussed herein in connection with the techniques depicted inFIGS.1-7. In various embodiments, a computing device or apparatus, such as computing device800or any combination of computing devices800, may be configured as any entity/entities as discussed for the techniques depicted in connection withFIGS.1-7in order to perform operations of the various techniques discussed herein. For example, computing device800may represent computer workstation102.

In at least one embodiment, the computing device800may be any apparatus that may include one or more processor(s)802, one or more memory element(s)804, storage806, a bus808, one or more network processor unit(s)810interconnected with one or more network input/output (I/O) interface(s)812, one or more I/O interface(s)814, and control logic820. In various embodiments, instructions associated with logic for computing device800can overlap in any manner and are not limited to the specific allocation of instructions and/or operations described herein.

In at least one embodiment, processor(s)802is/are at least one hardware processor configured to execute various tasks, operations and/or functions for computing device800as described herein according to software and/or instructions configured for computing device800. Processor(s)802(e.g., a hardware processor) can execute any type of instructions associated with data to achieve the operations detailed herein. In one example, processor(s)802can transform an element or an article (e.g., data, information) from one state or thing to another state or thing. Any of potential processing elements, microprocessors, digital signal processor, baseband signal processor, modem, PHY, controllers, systems, managers, logic, and/or machines described herein can be construed as being encompassed within the broad term ‘processor’.

In at least one embodiment, memory element(s)804and/or storage806is/are configured to store data, information, software, and/or instructions associated with computing device800, and/or logic configured for memory element(s)804and/or storage806. For example, any logic described herein (e.g., control logic820) can, in various embodiments, be stored for computing device800using any combination of memory element(s)804and/or storage806. Note that in some embodiments, storage806can be consolidated with memory element(s)804(or vice versa), or can overlap/exist in any other suitable manner.

In at least one embodiment, bus808can be configured as an interface that enables one or more elements of computing device800to communicate in order to exchange information and/or data. Bus808can be implemented with any architecture designed for passing control, data and/or information between processors, memory elements/storage, peripheral devices, and/or any other hardware and/or software components that may be configured for computing device800. In at least one embodiment, bus808may be implemented as a fast kernel-hosted interconnect, potentially using shared memory between processes (e.g., logic), which can enable efficient communication paths between the processes.

In various embodiments, network processor unit(s)810may enable communication between computing device800and other systems, entities, etc., via network I/O interface(s)812(wired and/or wireless) to facilitate operations discussed for various embodiments described herein. In various embodiments, network processor unit(s)810can be configured as a combination of hardware and/or software, such as one or more Ethernet driver(s) and/or controller(s) or interface cards, Fibre Channel (e.g., optical) driver(s) and/or controller(s), wireless receivers/transmitters/transceivers, baseband processor(s)/modem(s), and/or other similar network interface driver(s) and/or controller(s) now known or hereafter developed to enable communications between computing device800and other systems, entities, etc. to facilitate operations for various embodiments described herein. In various embodiments, network I/O interface(s)812can be configured as one or more Ethernet port(s), Fibre Channel ports, any other I/O port(s), and/or antenna(s)/antenna array(s) now known or hereafter developed. Thus, the network processor unit(s)810and/or network I/O interface(s)812may include suitable interfaces for receiving, transmitting, and/or otherwise communicating data and/or information in a network environment.

Variations and Implementations

In summary, in some aspects, the techniques described herein relate to a method including: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an artificial intelligence (AI) model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in the technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

In some aspects, the techniques described herein relate to a method, the method further includes: for each iteratively adding, mapping the user attributes to the topic to identify the topically-relevant user attributes as particular user attributes among the user attributes that match or most closely align to the topic expressed in the query.

In some aspects, the techniques described herein relate to a method, wherein: mapping includes searching the user attributes in a user profile of the user that includes information that conveys technical knowledge and skill sets of the user related to the topic.

In some aspects, the techniques described herein relate to a method, wherein: the user profile includes a corporate directory that includes location and company role information about the user, a list of technical publications on the topic authored by the user, and Internet postings on the topic authored by the user.

In some aspects, the techniques described herein relate to a method, the method further includes: upon determining that the response comprehension levels are less than the user comprehension level, continuing iteratively adding and receiving the iterative responses to expand the iterative responses.

In some aspects, the techniques described herein relate to a method, further including: evaluating the response comprehension levels of the iterative responses based on a combination of reading comprehension levels of the iterative responses and quantities of technical details included in the iterative responses.

In some aspects, the techniques described herein relate to a method, wherein: evaluating the response comprehension levels further includes increasing the response comprehension levels with increasing reading comprehension levels, and increasing the response comprehension levels with increasing quantities of the technical details.

In some aspects, the techniques described herein relate to a method, the method further includes: upon determining that iteratively adding the topically-relevant user attributes has exhausted the topically-relevant user attributes among the user attributes, stopping iteratively adding and receiving the iterative responses, wherein determining the final response includes determining as the final response a last iterative response.

In some aspects, the techniques described herein relate to an apparatus including: one or more network processor units to communicate over one or more networks; and a processor coupled to the one or more network processor units and configured to perform: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an artificial intelligence (AI) model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in the technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to perform: for each iteratively adding, mapping the user attributes to the topic to identify the topically-relevant user attributes as particular user attributes among the user attributes that match or most closely align to the topic expressed in the query.

In some aspects, the techniques described herein relate to an apparatus, wherein: the processor is configured to perform mapping by searching the user attributes in a user profile of the user that includes information that conveys technical knowledge and skill sets of the user related to the topic.

In some aspects, the techniques described herein relate to an apparatus, wherein: the user profile includes a corporate directory that includes location and company role information about the user, a list of technical publications on the topic authored by the user, and Internet postings on the topic authored by the user.

In some aspects, the techniques described herein relate to an apparatus, the processor is further configured to perform: upon determining that the response comprehension levels are less than the user comprehension level, continuing iteratively adding and receiving the iterative responses to expand the iterative responses.

In some aspects, the techniques described herein relate to an apparatus, wherein the processor is further configured to perform: evaluating the response comprehension levels of the iterative responses based on a combination of reading comprehension levels of the iterative responses and quantities of technical details included in the iterative responses.

In some aspects, the techniques described herein relate to an apparatus, wherein: the processor is configured to perform evaluating the response comprehension levels by increasing the response comprehension levels with increasing reading comprehension levels, and increasing the response comprehension levels with increasing quantities of the technical details.

In some aspects, the techniques described herein relate to an apparatus, the processor is further configured to perform: upon determining that iteratively adding the topically-relevant user attributes has exhausted the topically-relevant user attributes among the user attributes, stopping iteratively adding and receiving the iterative responses, wherein the processor is configured to perform determining the final response by determining as the final response a last iterative response.

In some aspects, the techniques described herein relate to a non-transitory computer readable medium encoded with instructions that, when executed by a processor, cause the processor to perform: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an artificial intelligence (AI) model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in the technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

In some aspects, the techniques described herein relate to a non-transitory computer readable medium, further including instructions to cause the processor to perform: for each iteratively adding, mapping the user attributes to the topic to identify the topically-relevant user attributes as particular user attributes among the user attributes that match or most closely align to the topic expressed in the query.

In some aspects, the techniques described herein relate to a non-transitory computer readable medium, wherein: the instructions to cause the processor to perform mapping include instructions to cause the processor to perform searching the user attributes in a user profile of the user that includes information that conveys technical knowledge and skill sets of the user related to the topic.

In some aspects, the techniques described herein relate to a non-transitory computer readable medium, wherein: the user profile includes a corporate directory that includes location and company role information about the user, a list of technical publications on the topic authored by the user, and Internet postings on the topic authored by the user.