Let's build a complete RAG pipeline from scratch. We'll cover each step with practical guidance and code concepts.

Step 1: Document Ingestion

First, collect and prepare your source documents:

• PDFs — Extract text with libraries like PyPDF2, pdfplumber, or Unstructured
• Web pages — Scrape with BeautifulSoup or use Firecrawl for AI-optimized extraction
• Markdown/Text — Read directly
• Databases — Export relevant records as text
• APIs — Pull data from Confluence, Notion, Google Docs, etc.

Tip: Clean your documents. Remove headers/footers, navigation elements, and boilerplate. Garbage in = garbage out.

Step 2: Chunking

Split documents into smaller pieces that fit in the LLM's context and contain focused information:

Document: "Company Handbook" (50 pages)
    ↓ Split into chunks
Chunk 1: "Vacation Policy" (300 tokens)
Chunk 2: "Remote Work Guidelines" (400 tokens)
Chunk 3: "Expense Reimbursement" (350 tokens)
... (150 more chunks)

Chunking parameters to tune:

• Chunk size — 200-1000 tokens (start with 500)
• Chunk overlap — 50-200 tokens (start with 100)
• Splitting method — By paragraph, by heading, by sentence, or recursive

Step 3: Generate Embeddings

Convert each chunk into a vector embedding:

Chunk: "Employees receive 20 days PTO per year..."
    ↓ Embedding model
Vector: [0.12, -0.45, 0.78, ..., 0.33] (1536 dimensions)

Store the vector alongside metadata:

• Source document name
• Page number or section
• Date of last update
• Any relevant tags or categories

Step 4: Store in Vector Database

Insert all chunk vectors + metadata into your vector database. Most databases handle indexing automatically.

Step 5: Query Pipeline

When a user asks a question:

1. Embed the query — Convert the user's question to a vector
2. Search — Find the top K most similar chunks (K = 3-10)
3. Filter — Optionally filter by metadata (date, source, category)
4. Re-rank (optional) — Use a cross-encoder model to re-score results
5. Construct prompt — Insert retrieved chunks into the system prompt
6. Generate — Send to the LLM for answer generation

Step 6: Prompt Construction

A good RAG prompt template:

System: You are a helpful assistant. Answer questions based ONLY
on the provided context. If the context doesn't contain enough
information, say "I don't have enough information to answer that."
Always cite your sources.

Context:
[Retrieved Chunk 1 - Source: handbook.pdf, p.12]
[Retrieved Chunk 2 - Source: policy-update.md]
[Retrieved Chunk 3 - Source: faq.pdf, p.3]

User: {user's question}

Common Tools and Frameworks

• LangChain — Full-featured Python/JS framework for RAG pipelines
• LlamaIndex — Specialized for document indexing and retrieval
• Haystack — Open-source NLP framework with RAG components
• Vercel AI SDK — TypeScript-first AI framework with RAG support
• Superbase pgvector — Combine your app database with vector search

Quick Start: Minimum Viable RAG

For a fast prototype, use:

1. Google NotebookLM — Upload docs, ask questions. Zero code.
2. ChatGPT file uploads — Drag PDFs into a conversation.
3. Claude Projects — Upload to Project Knowledge for persistent RAG.

These let you validate whether RAG is useful for your use case before building a custom pipeline.