Building Your First RAG Pipeline: LangChain, Embeddings, and Vector DBs

By mid-2024, RAG was on every AI roadmap slide — and for good reason. Teams needed LLMs that cited internal docs without retraining. The pattern was simple; the engineering was not.

The 2024 RAG stack

A typical pipeline looked like:

1. Ingest — PDFs, Confluence, Notion → clean text
2. Chunk — 512–1024 token windows with overlap
3. Embed — text-embedding-3-small, BGE, or E5 models
4. Index — Pinecone, Weaviate, Chroma, or pgvector
5. Retrieve + generate — top-k chunks fed into GPT-4 or Llama 3

LangChain and LlamaIndex dominated tutorials; production teams increasingly stripped down to custom Python once they understood the moving parts.

Chunking mattered more than the LLM

The biggest 2024 lesson: bad chunks = bad answers. Teams that skipped layout-aware parsing (tables, headers) saw hallucinations even with GPT-4.

Practices that worked:

• Respect document structure (H1/H2 boundaries)
• Metadata filters (department, product line, date)
• Hybrid search — BM25 + vectors — for keyword-heavy queries

Takeaways

• RAG in 2024 was a data engineering problem wearing an AI costume.
• Start small: one doc collection, 50 eval questions, one embedding model.
• Plan for re-indexing when docs change weekly.

Ready to go deeper? Deepskilling capstones include end-to-end RAG on AWS and Neon — see training programs.