How to Build a RAG App: Step-by-Step Guide for Beginners 2026

Quick Answer

RAG lets LLMs answer questions using your documents. Embed chunks, store in pgvector or Qdrant, retrieve top-k with reranking, then pass to the LLM as context. Always cite sources in the response.

• Chunk size of 500-1000 tokens works for most cases
• Reranking (Cohere, BGE) improves quality by 20-40%
• Always display citations — hallucinations kill trust

What You'll Need

• Document corpus (PDFs, markdown, web pages)
• Embedding model (text-embedding-3-small, bge-m3, or assisters-embed)
• Vector DB: pgvector, Qdrant, Weaviate, or Chroma
• LLM via OpenAI-compatible API

Steps

1. Ingest and chunk. Use unstructured or langchain for PDFs. Chunk at 800 tokens with 100 overlap.
2. Embed. Batch embed chunks:

   const { data } = await ai.embeddings.create({
     model: 'assisters-embed-v1',
     input: chunks,
   });

3. Store in pgvector. INSERT INTO documents (content, embedding) VALUES (...)
4. Create index. CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
5. Query pipeline. Embed user question, vector search top 20, rerank to top 5.
6. Rerank. Use Cohere Rerank or BGE reranker:

   const { results } = await ai.rerank.create({
     query,
     documents: candidates,
     top_n: 5,
   });

7. Prompt the LLM. System: Answer using only the provided context. Cite sources with [n].
8. Return with citations. Link back to original documents.

Common Mistakes

• Bad chunking. Splitting mid-sentence destroys meaning. Use semantic chunking.
• No reranking. First-pass vector search is noisy.
• Losing metadata. Always keep doc_id, title, url.
• Ignoring recency. Add time decay for news/social corpora.

Top Tools

Conclusion

RAG is the dominant pattern for domain-specific AI in 2026. Start with pgvector + Assisters, add reranking, always cite. Misar Dev builds full RAG stacks in minutes.