Questions
"1. Introduction and touch up on gen ai use cases worked
2. Given an example and output schema, Give me the steps that youll follow to get the output
3. How will you reduce latency
4. How will you calculate accuracy of System & make sure that its adheres to schema"
"1. Introduction
2. A free text input containing hashtags, emoticons, and mixed content was provided. The task for the Large Language Model (LLM) was to extract specific information (such as product names) and conduct sentiment analysis. The output had to be formatted in a structured JSON format that included the product name and sentiment, categorized as positive (+), negative (-), or neutral.

- prompt
- system
- instructions
- examples
- parameters/settings
- systematic experimentation
- force format adherence"
"1. Introduction and  touch up on gen ai use cases worked
2. Project specific question - Challenges faced
3. Give sample product review, desired output.
   3.1 Experiments to find best value for temp, max o/p, top p, top k
   3.2 Evaluation metrics of the output
   3.3. Output Validation"
1. Introduction 2. How do you enforce json schema on the output of LLM 3. How do you measure latency of LLM ?
 1. Introduction 2. How will you finalize a model for your use case (based on one of the project i have worked on)  3. Yesterdays question on how we can get the desired schema 4. How can you do text extraction from PDF especially tables and images from PDF 5. How do you measure latency of LLMs?
"1. Introduction
2. How you will evaluate 2 set of embeddings
3. How would you extract the tables from the PDF document?
4. How do you measure the latency of LLMs?"
"1. Introduction
2. How to get the desired schema in the output?
3. How would you extract the tables from the PDF document?
4. How do you measure the latency of LLMs?"
 1. Intro 2.Asked to me explain the projects in details 3.How you will evaluate 2 set of embeddings from 2 different model for a flipkart where a creatin item is listed on 1 page and from 2nd model on 4th page , how is the recommendation working?
"1. Intro
2. Project Related Questions (model deployment, Embedding search, evaluation)
3. Write a logic to get data from kafka transaction stream and find the traffic for particular zipcode in lasr 30 minutes
4. Write a logic to find a score of varienty of content user is watching based on his recent behaviour
5. ever worked on streamlit?"
"1. Introduction 
2. Started with Traditional ML project with detailed explanation - What was the problem? , Which Model we have used?
3. Gen AI project discussion - Architecture Discussion
4. Which technologies you have used?
5. How would you evaluate RAG pipeline?
6. What all the metrics are there in RAGAS?
6.1 Explain Context_precision metric.
7. What is Reciprocal Rank Fusion ?
8. How would you extract the Graphs and tables from the PDF document?
9. How will you write a prompt that can generate exactly 2000 Words for an essay on a given topic Eg.  “Bertrand Russel’s - a history of western philosophy” Not the complete prompt just restrict LLM to write essay exaclty with 2000 words"
"1. Introduction
2. Most challenging technical task faced 
3. Question related to feature engineering:

Feature Creation Challenge:  Content Variety Score for Churn Prediction
You are working on a churn prediction model for a streaming TV service. You suspect that users who watch a wider variety of content are less likely to churn.  You have access to the following ""Watch Data"":

| Field Name      | Data Type | Description                               |
|-----------------|-----------|-------------------------------------------|
| client_id      | Integer   | Unique identifier for the customer       |
| start_time      | Integer   | Unix timestamp of the viewing session start|
| program_id     | String    | Unique identifier for the program watched |
| minutes_watched | Integer   | Number of minutes watched                 |
| thumbs_up       | String    | User feedback ('Y' for yes, 'N' for no, null for no feedback) |

Your task: Design and describe a single feature, called content_variety_score, that quantifies the diversity of content watched by each user. This feature should be designed in such a way that a higher score indicates greater variety in viewing habits.

Consider the following in your design:

Time Decay:  Recent viewing patterns should be weighted more heavily than older viewing patterns. How will your feature incorporate the start_time information to reflect this?
Scale and Normalization: The feature should be comparable across users, regardless of how much they watch overall. How will you ensure the score is appropriately scaled and normalized?

Deliverables:

A precise definition of the content_variety_score.  Include a formula or pseudocode that clearly demonstrates how it is calculated.
"
"1. Introduction.
2. Explained project.
3. How to compare two LLM's
Question 1:
Example Data Stream (Illustrative - Freeform Text Snippets):

Freeform Text: 

Context: 
""OMG, the new ✨SparklePhone Pro✨ is AMAZING! 😍 The camera is insane. #SparklePhone #BestPhoneEver"" ""Just got my SparklePhone Pro, and the battery life is terrible. 😠  Two hours and it's dead.  Don't buy! #SparklePhone #TechFail"" ""Anyone know if the SparkleWatch has GPS? 🤔  Thinking of getting one for running. #SparkleWatch #FitnessTracker"" ""SparkleTab is on sale! 🎉  $299 for the 128GB model.  Such a steal! #SparkleTab #TabletDeals"" ""The SparklePhone Pro's camera is 🔥🔥🔥. So much better than my old phone. #SparklePhone #Photography"" ""SparkleWatch is great for tracking my workouts! 💪 #SparkleWatch #FitnessMotivation"" ""Hate the SparkleTab's keyboard.  So clunky. 😒 #SparkleTab #TechProblems"" …


Desired Output:
[
  {""product"": ""SparklePhone Pro"", ""sentiment"": ""positive"", ""mentions"": 2},
  {""product"": ""SparklePhone Pro"", ""sentiment"": ""negative"", ""mentions"": 1},
  {""product"": ""SparkleWatch"", ""sentiment"": ""neutral"", ""mentions"": 1},
  {""product"": ""SparkleTab"", ""sentiment"": ""positive"", ""mentions"": 1},
  {""product"": ""SparklePhone Pro"", ""sentiment"": ""positive"", ""mentions"": 3},
  {""product"": ""SparkleWatch"", ""sentiment"": ""positive"", ""mentions"": 2},
  {""product"": ""SparkleTab"", ""sentiment"": ""negative"", ""mentions"": 1}
]

Output Rules:
Must be List of JSON / Dicts of products
Each product must have the fields sentiment, mentions, product
Sentiment must be [positive, negative or neutral]
Mentions must be an Integer , must be >= 0
List length can be variable
Product cannot be made up 

Goal is to 
Format & rules must be adhered to at all costs
reduce Perceived latency to the least ‘ms’ possible


PROMPT: 
- SYSTEM : You are expert in extracting information from text
- INSTRUCTIONS: Carefully read the paragraph and extract product name and sentiment.
Output format should be :

[
  {""product"": ""<Product name>"", ""sentiment"": ""<positive, Negative, Neutral>"", ""mentions"": '<number>'},
 
]
- EXAMPLES :
        This will be the example of input format .
""OMG, the new ✨SparklePhone Pro✨ is AMAZING! 😍 The camera is insane. #SparklePhone #BestPhoneEver"" ""Just got my SparklePhone Pro, and the battery life is terrible. 😠  Two hours and it's dead.  Don't buy! #SparklePhone #TechFail"" ""Anyone know if the SparkleWatch has GPS? 🤔  Thinking of getting one for running. #SparkleWatch #FitnessTracker"" ""SparkleTab is on sale! 🎉  $299 for the 128GB model.  Such a steal! #SparkleTab #TabletDeals"" ""The SparklePhone Pro's camera is 🔥🔥🔥. So much better than my old phone. #SparklePhone #Photography"" ""SparkleWatch is great for tracking my workouts! 💪 #SparkleWatch #FitnessMotivation"" ""Hate the SparkleTab's keyboard.  So clunky. 😒 #SparkleTab #TechProblems"" …

based on this output should be like:
[
  {""product"": ""SparklePhone Pro"", ""sentiment"": ""positive"", ""mentions"": 2},
]
Based on user input 
- PARAMETERS / SETTINGS:
- Temp : 0
- Max_Tokens 1024
- Top_P : 0.9
- Top_K: 0.1
- Freq/Presence_Penalty 
....
 
- SYSTEMATIC EXPERIMENTATION
- FORCE FORMAT ADHERENCE={
""product"":{
type:str},
sentiment:{type:str},
mentions:

}



import ast
ast.litral_eval(response)



 "
 
 analyse the questions and find out what skills they are looking at