RAG & Vector Search
Intermediate
Signal 93/100
What is Retrieval-Augmented Generation (RAG)?
by IBM Technology
Teaches AI agents to
Build a production RAG pipeline with document chunking, embedding, and retrieval
Key Takeaways
- Complete RAG implementation from scratch
- Covers document chunking strategies
- Implements embedding and vector search
- Builds a Q&A system over custom documents
- Evaluates retrieval quality
Full Training Script
# AI Training Script: What is Retrieval-Augmented Generation (RAG)? ## Overview • Complete RAG implementation from scratch • Covers document chunking strategies • Implements embedding and vector search • Builds a Q&A system over custom documents • Evaluates retrieval quality **Best for:** Engineers building knowledge bases and document Q&A systems with LLMs **Category:** RAG & Vector Search | **Difficulty:** Intermediate | **Signal Score:** 93/100 ## Training Objective After studying this content, an agent should be able to: **Build a production RAG pipeline with document chunking, embedding, and retrieval** ## Prerequisites • Working knowledge of RAG & Vector Search • Prior hands-on experience with related tools • Comfortable with technical documentation ## Key Tools & Technologies • OpenAI Embeddings • Pinecone • LangChain • Python ## Key Learning Points • Complete RAG implementation from scratch • Covers document chunking strategies • Implements embedding and vector search • Builds a Q&A system over custom documents • Evaluates retrieval quality ## Implementation Steps [ ] Study the full tutorial [ ] Identify the main tools: OpenAI Embeddings, Pinecone, LangChain, Python [ ] Implement: Build a production RAG pipeline with document chunking, embedding, and retrieval [ ] Test with a real example [ ] Document what you learned ## Agent Execution Prompt Watch this video about rag & vector search and implement the key techniques demonstrated. ## Success Criteria An agent completing this training should be able to: - Explain the core concepts covered in this tutorial - Execute the demonstrated workflow with OpenAI Embeddings - Troubleshoot common issues at the intermediate level - Apply the technique to similar real-world scenarios ## Topic Tags openai embeddings, pinecone, langchain, python, rag-&-vector-search, intermediate ## Training Completion Report Format - **Objective:** [What was learned from this content] - **Steps Executed:** [Specific implementation actions taken] - **Outcome:** [Working demonstration or artifact produced] - **Blockers:** [Technical issues encountered] - **Next Actions:** [Follow-up tutorials or practice tasks]
This structured script is included in Pro training exports for LLM fine-tuning.
Execution Checklist
[ ] Watch the full video [ ] Identify the main tools: OpenAI Embeddings, Pinecone, LangChain, Python [ ] Implement the core workflow [ ] Test with a real example [ ] Document what you learned
