Advanced AI: RAG, MCP, and Agents

Data Literacy

AI Workflows

  • Process information from unstructured to structured data.
  • Finding Aid for complex information.
  • RAG (Retrieval-Augmented Generation)
  • MCP (Multi-Chain Prompting)
  • Agents (AI Agents)

RAG: Retrieval-Augmented Generation

  • Process Data.
  • Chunking.
  • Embeddings.
  • Vector Databases.
  • LLMs (Large Language Models).

MCP: Model Context Protocol

  • A standard protocol.
  • Set of instructions for LLMs to follow.
  • Gives LLM way to use external tools.
  • LLMs can use multiple tools in a single prompt.
  • Access third-party APIs.
  • How to query databases.

Agents: AI Agents

  • LLMs can act as agents.
  • Agents can perform tasks on behalf of users.
  • Agents can use tools and APIs to complete tasks.

Agents: AI Agents (Continued)

  • Autonomously complete task at predetermined interval:
    • Check for new data.
    • Process data.
    • Generate report.
  • Big Agents = Bad Agents

Breaking down a Workflow

  • Break down a workflow into steps.
  • Ask AI to do small jobs.
  • Use deterministic scripts when possible.

Example 1: Indexing an Archive

  • Step 1: Take Photos of Documents.
    • Human can include narrative to contextualize document.
  • Step 2: Convert Photos to Text (OCR).
  • Step 3: Chunk Text into Sections, if necessary.
  • Step 4: Agent Extracts Metadata from Text.

Example 1: Indexing an Archive (Continued)

  • Step 5: Agent Summarizes Text and Metadata.
  • Step 6: Parse Metadata and Summaries into structured data.
  • Step 7: Store structured data in a database.
  • Step 8: Human reviews and corrects structured data.

Example 2: Vectorized Database

  • Step 1: Collect sources.
  • Step 2: OCR sources.
  • Step 3: Chunk text into sections.
  • Step 4: Create embeddings for each chunk.
  • Step 5: Store embeddings in a vector database along with Metadata.
  • Step 6: Use RAG to query vector database.