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Understanding Retrieval Augmented Generation

LLMs are trained on a vast amount of textual data, and their capabilities are based on the knowledge they acquire from this data.

This means that if you ask them a question about data that is not part of their training set, they will not be able to respond accurately, resulting in either a refusal (Where the llm responds with “i dont know”) Or worse, a hallucination.

So, how can you build a genai application that would be able to answer questions using a custom or private dataset that is not part of the llm’s training data?

RAG Flow: A Step-by-Step representation

Data Ingestion

  • Blue arrows show the flow of data from various sources (databases, cloud, etc.) for Retrieval Augmented Generation.
  • Text-based GenAI applications process data, translating and extracting text as needed.

Text Processing

  • Extracted text is divided into chunks and processed using Vectara’s Boomerang model to create vector embeddings.

Query-Response Flow

  • Green arrows illustrate the user query and response process.
  • Query encoding and approximate nearest neighbor search retrieve relevant text chunks for response.

Prompt and Generation

  • Relevant text chunks construct a comprehensive prompt for generative language models like OpenAI.
  • Language models ground responses in provided facts, avoiding hallucination.

Validation and User Response

  • Optionally, responses can undergo validation before being sent back to the user.

Enterprise Automation (Optional)

  • Red arrow indicates the optional step of taking action based on trusted responses, like automated tasks in enterprise systems.

~ Case Studies~

Generative AI Case Studies

Chatbot Development

Overview: Designed an intelligent chatbot to improve user interaction

Parameters: Approximately 175 billion (GPT-3 standard).
Vectors: Custom embedding vectors for industry context.
Hardware: Cloud-based AI-optimized compute instances.
Software: OpenAI API, Python,  Langchain, Reis and Chroma. 

Challenges: Achieving a human-like conversational experience with accurate context understanding.

Solution: Implemented a GPT-3.5 based conversational agent with custom fine-tuning for industry-specific knowledge.  ML Model: GPT-3.5 with domain-specific fine-tuning.

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