Building Production RAG Systems: Lessons from the Field

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI systems that need to reason over private data. The concept is simple: retrieve relevant context, then generate responses grounded in that context.

The implementation is anything but simple.

After building RAG systems across fintech, healthcare, and enterprise SaaS, here are the lessons that tutorials do not teach you.

The Chunking Problem

Every RAG tutorial tells you to "chunk your documents." None of them tell you that chunking is where most RAG systems fail.

Chunk Size Tradeoffs

• Too small (< 200 tokens): Chunks lack context. The LLM cannot understand what it is reading.
• Too large (> 1000 tokens): Retrieval becomes imprecise. Relevant information gets buried in noise.
• Sweet spot: 300-500 tokens for most use cases, but this varies by domain.

Semantic Chunking

Do not chunk by character count. Chunk by meaning:

1. 1Split on paragraph boundaries first
2. 2Merge small paragraphs that belong together
3. 3Split large paragraphs at sentence boundaries
4. 4Preserve document structure (headers, lists, tables)

Overlapping Chunks

Include 10-20% overlap between chunks to preserve context that spans chunk boundaries. This prevents the "split sentence" problem where relevant information is cut in half.

Retrieval Is Everything

The best LLM in the world cannot help you if you retrieve the wrong context. Invest heavily in retrieval quality.

Hybrid Search

Pure vector similarity search misses keyword matches. Pure keyword search misses semantic similarity. Combine both:

final_score = alpha * vector_score + (1 - alpha) * bm25_score

Start with alpha = 0.7 and tune based on your data.

Reranking

Initial retrieval should be fast and return many candidates (20-50). Then use a cross-encoder reranker to select the top 3-5. This two-stage approach gives you both speed and precision.

Query Transformation

User queries are often vague or poorly structured. Transform them before retrieval:

• Query expansion: Add synonyms and related terms
• Hypothetical document embedding (HyDE): Generate a hypothetical answer, then use that for retrieval
• Query decomposition: Break complex queries into sub-queries

The Context Window Trap

Just because you can fit 128K tokens in the context window does not mean you should.

Why More Context Hurts

• Lost in the middle: LLMs perform worse on information in the middle of long contexts
• Latency: More tokens = slower responses = higher costs
• Noise: Irrelevant context actively degrades response quality

Right-Sizing Context

Aim for 3-5 highly relevant chunks rather than 10-15 marginally relevant ones. Quality beats quantity every time.

Evaluation Is Non-Negotiable

You cannot improve what you cannot measure. Build evaluation into your RAG system from day one.

What to Measure

Building Evaluation Sets

Create a golden dataset of 50-100 query-answer pairs with human-validated correct answers. Use this for regression testing as you iterate.

Augment with LLM-as-judge evaluations for scale, but always validate against human judgment.

Production Considerations

Caching

Cache at multiple levels:

• Embedding cache for repeated queries
• Retrieval cache for common queries
• Response cache for identical query + context combinations

Monitoring

Track in production:

• Retrieval latency (p50, p95, p99)
• LLM latency (p50, p95, p99)
• Retrieval hit rate
• User feedback signals (thumbs up/down)
• Cost per query

Failure Modes

Design for graceful degradation:

• What happens when no relevant documents are found?
• What happens when the LLM refuses to answer?
• What happens when the answer contradicts retrieved context?

The Meta-Lesson

RAG systems are not "set and forget." They require continuous iteration:

1. 1Monitor retrieval and response quality
2. 2Identify failure cases
3. 3Improve chunking, retrieval, or prompts
4. 4Measure the impact
5. 5Repeat

The teams that win at RAG are the teams that invest in evaluation infrastructure and iterate relentlessly.