Shipping RAG and AI Automation in Production

Retrieval-augmented generation looks simple in a demo and fragile in production. The gap is almost never the model — it is chunking, retrieval quality, and whether you can tell when the system is wrong. Techbylanz Business Solutions builds AI automation and RAG systems with those production constraints in mind from day one.

Start with evals, not embeddings

Before you tune chunk size or swap vector databases, write a gold set of questions with expected answers or citations. Every change should move that score. Without evals you are optimizing vibes — not business outcomes.

• Keep 50–200 real user questions with source docs labeled.
• Score citation correctness separately from answer fluency.
• Gate deploys on eval regression, not just latency.

Chunking that respects structure

Fixed 512-token windows discard headings, tables, and code boundaries. Prefer structure-aware splits: Markdown sections, HTML landmarks, or semantic paragraphs with overlap only where cross-references matter.

Hybrid retrieval beats pure vectors

Combine sparse (BM25 / keyword) with dense embeddings, then re-rank. Product SKUs, error codes, and proper nouns often fail pure semantic search. Hybrid retrieval plus a small re-ranker is the pattern we ship most often for client knowledge bases.

Observe the retrieval path

Log query → retrieved IDs → final answer. When users report bad answers, you need to know if retrieval missed or the model ignored good context. That pipeline log saves weeks of guessing — and is standard in every Techbylanz AI automation engagement.