Use cases for Langchain in your business
Tags: ai, promptengineering, automation, llm
Published: 2024-03-16 22:04:53

**Unlocking LangChain's Power for Financial Institutions**

Let's explore the business opportunities of GenAI, LLms and a framework like Langchain.
Let's look at some categories of use-cases for the Large Language Models linked to real world scenarios.


## The four pillars of AI toolkits


![GenAI toolkit principles, Langchain, python programming, summarizing, inferring, transforming, expanding](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ea6qrl747z7cwdgw940m.jpg)
**Summarizing**
Think about at the huge amount of documentation collected in every company, not only simple word files but excel, pdf, video, images, confluence page. Large Language Models can be used to summarize text, images or videos. It can help in saving time and gaining insights from a large volume of content centralizing the search in a single endpoint. These models can be used to summarize documentation of a project, meetings reviews or customer feedbacks, and actions can be automated based on the responses, customize the desired result.
We can create prompts to analyze multiple reviews of the same product or multiple reviews of multiple products. We can also focus on specific aspects of input data for summarization. For example, through prompt engineering, we can summarize customer feedbacks to find about product quality or shipping and handling or operational issues, etc. and automate processing of these feedbacks.

**Inferring**

Inference involves analyzing input data to extract labels, names, or sentiment. Large Language Models can be used to process data to identify specific entities. They can identify customer sentiments from reviews, topics from articles, etc.
In the example of customer feedbacks, we can create prompts to identify if a customer has posted a feedback about a particular product and build automation around it to route it to specific department.
The benefit of Large Language Models is that we don't need to collect training data, train a model or maintain it. We can perform the task of inference with just some prompt engineering.

**Transforming**

Large Language Models can be used for transforming text they can perform language translation on real/time by request, spelling and grammar correction, format transformation, and tone transformation, all with just the help of prompts. This capability can be used for internationalization of a website, to recognize offensive words, as an example. Also, Large Language Models can be used in communications and respond to customers more effectively. 
Transformation is a very versatile capability of Large Language Models which can have multiple use-cases.
From a technical perspective this could be used also to automatically translate encoded text into human readble text.

**Expanding (Augmentation)**

Expanding is the task of taking a shorter piece of data, and having generate a longer piece of data from it. It is also named as "augmentation" a tecnique used to increase the number of training or testing material that the model has asvailable. 
It takes the tokens from the input and expand or generate tokens that'd follow the ones in the input. This is a very powerful category of use-cases of these models.
We can create prompts to generate content using the Large Language Models. It can be used to create articles, blogs, stories, poems, emails, images, videos, etc. It can have a wide variety of applications, like analyzing healthcare literature and helping with diagnosis, providing students with detailed explanations and answering questions in real-time, helping customers with query resolutions and interact through chatbots, role-playing and helping with brainstorming or interview preparations, etc.

## Specific Real Use-Cases in everyday business



![Usecases for business regarding large language models in finance and banking](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tze3t4r4ainzn8qhab8n.png)

Having a framework that is able to seamlessly connecting language models with diverse data sources, is obviovsly powerful. 

Its intuitive features allow financial managers to be able composing customized tools that leverage its capabilities without the need for intricate technical engineering expertise. Let's just think about how could be important to have the last updated stock data organized in an autogenerated and customized report, automated for the everydays. 

Let's delve deeper into some practical use cases, with vivid examples and extensive benefits, specifically crafted for the dynamic field of finance:

**In-depth Document Analysis**

_Example_: 
Imagine a mortgage department in a bank seeking insights from a vast array of loan documents. A GenAI tool can facilitates the development of language-powered applications that can generate answers to specific questions about these documents.

_Benefits_: 
This empowers financial institutions to efficiently retrieve critical information, cite sources accurately for compliance purposes, and semantically search through intricate financial reports for nuanced details.

**Chatbots for Enhanced Customer Interaction**

_Example_:
Consider a scenario where a bank implements a chatbot to handle routine customer inquiries. 

LangChain simplifies the process by seamlessly integrating :  

- language models; 

- conversation templates;

- memory components. 

What this means ? 
The most repetitive actions could be collected and performed by a chatbot, for example like answering to questions regarding tax filling or how to create additional foreign currency account. 
Can help having an assistant that drive you defining IBAN and accounts with the correct format for CH, US or Asia, write Swift messages like MT940/1/2/50 that you can use for testing or compare the results.

Nowdays all those actions are performed by a human agent, a tester or  a developer after a request of a meeting from the customers.
Can create sentiment analysis to identify audience for you, Social media post creation and helping targeting, tiktok, linkedin, Facebook, instagram. reducing the cost to running those campaign.

_Benefits_:
 Finance professionals can create chatbots with distinct personalities tailored to different scenarios. For instance, a chatbot can serve as a helpful banking assistant, guiding customers through transaction processes or as an informative financial advisor providing insights on investment opportunities.


**Streamlined Text Summarization for Reports**

_Example_:
This is covered under the umbrella of 
Financial analysts dealing with extensive reports, such as quarterly financial summaries, can use LangChain and GenAi tools for efficient summarization. 
The framework breaks down complex documents into digestible chunks, can identify concept inside documents and give you the exact position or seek for similar documents to investigate deeper the topics, this could significantly aiding in decision-making processes.
_Benefits_: 
This functionality enhances productivity by quickly summarizing lengthy financial reports, newsletters, or customer feedback. It allows financial managers to focus on key insights and trends without getting bogged down by extensive documentation.

**Structured Information Extraction from Unstructured Text**

_Example_: 
Extracting relevant details from unstructured financial documents, like contracts or legal agreements. It enables the conversion of sentences into structured rows suitable for databases. A defined cleaning process is mandatory when working with machine learning and there is an important need to refine and correct wrong data that will be inserted in the data storage.
_Benefits_: 
Financial institutions can seamlessly insert extracted data into databases, transform lengthy legal documents into multiple rows for efficient storage, and accurately identify API parameters from user queries, ensuring precision and compliance.
A tidy up database will improve the correctness of the results and facilitate the effort to eliminate allucinations for future uses.

**Autonomous Agents for Operational Efficiency**

_Example_: 
Picture a scenario where a financial institution deploys an app to automate routine tasks. This could be an AI plugin retrieving real-time financial data or a context-aware agent assisting in sales interactions.
_Benefits_: 
Financial professionals can build custom AI plugins to retrieve information from various tools, integrate existing modules for diverse applications like shopping, travel, or marketing, and create agents that understand and respond to customer inquiries in a personalized and efficient manner.

**Effective Evaluation of Language Model Output**

_Example_: 
Assessing the quality of outputs generated by language models can be a challenge. LangChain addresses this with tools like Tracing and community datasets for robust evaluation.
The flexibility given by a framework binded with a popular programming language like python is powerful and solution driven. A (pre-post)processing data phase can always be added in any moment if you want to have more accuracy and traceability on the data inserted and produced.
_Benefits_: 
Financial institutions can thoroughly evaluate generative models, assess the performance of API chains such as OpenAPI, and ensure accurate results in question-answering tasks related to financial documents, vector databases, and SQL databases.
For a single use and 

**Efficient Querying of Tabular Financial Data**

_Example_: 
Consider a financial analyst needing to query extensive tabular data for market trends. LangChain offers solutions like document loaders and predefined chains for efficient querying of structured data.
_Benefits_: 
Financial professionals can effortlessly load and index data using tools like CSVLoader, start with simple queries using predefined chains, and scale up to handle complex databases with powerful agents. This ensures timely access to critical information for informed decision-making.
LangChain's user-friendly approach empowers finance professionals to unlock the full potential of language models, significantly enhancing operational efficiency, compliance, and decision-making processes within the dynamic financial landscape.