Retrieval and document AI service
RAG development for grounded product answers
I build RAG systems that connect product questions to approved source material. The design covers ingestion, retrieval, answer controls, citations, evaluation, and product behavior.
Updated September 2026
Quick answer
RAG development combines document processing, search, and model generation. A reliable system must retrieve the right evidence, show useful sources, handle weak retrieval, and pass representative evaluation cases before launch.
Evidence and decision points
Production RAG
Built retrieval workflows for large document processing and contextual multi-turn answers.
Product integration
Connected retrieval with chat interfaces, projects, files, persistent messages, and scoped memory.
Evaluation focus
Treats source quality, retrieval coverage, answer grounding, latency, and failure behavior as separate checks.
A RAG pipeline has several failure points
A fluent answer does not prove that retrieval worked. The system may select the wrong document, miss an important passage, or generate a confident response without enough evidence. Diagnosis must separate retrieval quality from answer quality.
I inspect the source format, extraction, chunk boundaries, metadata, indexing, query construction, retrieval, reranking, prompt context, and answer display. Each stage needs its own observable check.
- Extraction must preserve the text and structure needed for later retrieval.
- Chunking must reflect the document type instead of using one fixed size everywhere.
- Retrieval must return relevant evidence for representative questions and difficult edge cases.
- The answer should expose sources and admit when available evidence is weak.
Evaluation before expansion
A small evaluation set creates a stable baseline. It should include common questions, ambiguous language, missing answers, conflicting sources, and requests that cross access boundaries.
I use the baseline to compare changes to chunking, metadata, search, reranking, prompts, and models. This prevents one visible improvement from hiding a regression elsewhere.
- Record the expected source or acceptable evidence for each evaluation question.
- Measure retrieval and answer behavior separately.
- Include negative cases where the correct response is uncertainty or refusal.
- Keep latency and cost visible while improving answer quality.
Product controls around retrieval
Private content needs clear access boundaries. Retrieval should respect the current user, workspace, project, and document permissions before any source reaches a model.
The interface should help users understand which sources shaped an answer. Source links, excerpts, dates, and document names support review and correction.
RAG delivery options
| Need | Useful starting point | Expansion trigger |
|---|---|---|
| Small stable knowledge base | Simple indexed retrieval | Quality misses require better metadata or ranking |
| Mixed document formats | Format-aware extraction and chunking | New formats fail baseline cases |
| Private workspaces | Permission-scoped retrieval | More roles or sharing models appear |
| High-stakes answers | Sources, refusal states, and human review | Evidence requirements become stricter |
Sources and further reading
- NIST AI Risk Management Framework
A useful reference for documenting AI risks, controls, and evaluation responsibilities.
- Reddit RAG troubleshooting discussion
A public example of the evaluation and retrieval problems builders describe in their own language.
Common questions
Can you improve an existing RAG system?
Yes. I can begin with representative questions and trace failures through extraction, chunking, retrieval, ranking, prompting, and answer display.
Does every RAG project need a vector database?
No. The search design should match the content, scale, filters, and question patterns. Keyword or hybrid search can be more useful in some systems.
Can a RAG system prevent hallucinations?
It can improve grounding, but it cannot guarantee perfect answers. The product still needs evaluation, source display, uncertainty, and safe failure behavior.
Can you connect RAG to a Next.js product?
Yes. I can build the ingestion, retrieval, backend, streaming response, source interface, and workspace integration.
Related resources
Guides
RAG prototype to production
Turn a retrieval demo into a maintainable product with evaluation, data controls, observability, and clear user states.
Case studies
RAG document processing
I built document-processing workflows that support large source files, multi-turn answers, and persistent conversation context.
Services
AI SaaS development
I build full-stack AI products where models support a defined user workflow. The work can include interfaces, APIs, persistence, routing, tools, retrieval, and operating controls.
Discuss a defined project
Send the product context, current stack, expected outcome, constraints, and target timeline. I will reply with fit and next steps.
Email Amar