Retrieval-augmented generation (RAG) is a way to make an LLM answer from your own content. When a question comes in, the system retrieves the most relevant passages from your documents and data, and the model writes its answer from those passages, citing them. Your data stays in your systems and is not used to retrain the model.
RAG Development Services
RAG development for production: document ingestion, vector database selection and integration, hybrid search, evaluation and scaling. Answers cite your sources.
Retrieval-augmented generation (RAG) lets an LLM answer from your documents and data instead of from what it memorised in training. The system finds the relevant passages first, and the model answers from them with citations.
A RAG demo takes an afternoon. A RAG system people rely on needs clean ingestion, retrieval that respects permissions, an evaluation set, and infrastructure that stays fast under load. Lightrains builds that production layer, and has run RAG systems for fintech, legal and healthcare clients.
What Lightrains builds
Support knowledge bases
Answers for customers and support staff from help-centre articles, release notes and past tickets, updated as the product changes, without retraining a model.
Support agents built on RAG →Legal, compliance and policy search
Question answering over contracts, regulations and internal policy where every answer has to point at the clause it came from.
RAG vs fine-tuning for enterprise AI →Internal search across repositories
One search over wikis, drives, CRM notes and ticket systems, filtered by department, document version and what each user is allowed to read.
Enterprise context engineering →Report-grounded assistants
Assistants that answer only from one customer’s own record or report, and hand off to a specialist when a question needs judgement.
Case study: genomic report assistant →Results from production RAG systems
180 ms
p95 latency on a fintech RAG pipeline on Qdrant, down from 1,200 ms, with capacity for 200K queries a day.
$12K → $4K
Monthly infrastructure cost for the same pipeline after the redesign, with 99.9% uptime over six months.
34% → 96%
Citation rate for a healthcare client after moving to RAG, without retraining the model.
Sources: Scaling a RAG pipeline with Qdrant and RAG vs fine-tuning for enterprise AI.
What goes into a production RAG system
Most RAG failures are retrieval failures: the right passage was never found, was out of date, or was one the user should not have seen. Each of these is part of the build.
Ingestion that keeps up
Parsing, chunking and metadata for PDFs, HTML, tickets and database rows, with updates and deletions flowing through so stale content does not reach the model.
Hybrid retrieval and reranking
Keyword search, vector similarity and metadata filters combined, then reranked, because users rarely phrase a question the way the source document does.
Permission-aware results
Access rules from the source systems are applied at retrieval time, so the model never sees a document the user could not open themselves.
Answers with citations
Each answer links to the passages it used. When nothing relevant is found, the system says so instead of guessing.
Evaluation before launch
A test set built from real questions measures retrieval quality and answer quality, including hard cases such as conflicting or outdated documents.
Latency and cost control
Caching, right-sized models and a tuned vector cluster keep response times and infrastructure bills predictable as query volume grows.
Vector database selection and integration
The vector database stores the embeddings that retrieval searches. The right choice depends on project stage, scale and where it will run. Lightrains integrates all of these and picks based on your requirements, not vendor preference.
| Database | Usually the right choice for |
|---|---|
| ChromaDB | Local prototypes and proofs of concept. |
| Qdrant | Self-hosted production systems that need metadata filtering and predictable cost. |
| Pinecone | Production teams that prefer a fully managed service. |
| Milvus | Very large collections, beyond millions of embeddings. |
| Weaviate | Semantic search combined with relationships between records. |
| FAISS | Custom search pipelines that need algorithm-level control. A library, not a database: persistence and scaling are yours to build. |
The full comparison, with cost and scale guidance, is in how to choose a vector database for AI.
How a project runs
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Step 1
Discovery
Map the workflow, the users and the sources. Decide which source is authoritative for each kind of question and how access is controlled today.
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Step 2
Prototype on your data
Index a representative slice of your content and build the evaluation set. The prototype is judged on that set, not on a demo.
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Step 3
Production build
Full ingestion, permissions, monitoring and the chosen vector database in your environment, tested for accuracy, freshness and load.
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Step 4
Run and improve
Retrieval diagnostics and user feedback show where answers go wrong. New sources and workflows are added as the system proves itself.
When the system needs to act as well as answer, RAG becomes one part of an agent: see AI agent development and MCP server development for connecting agents to live systems.
Frequently Asked Questions
Choose RAG when your content changes often, when answers need source citations, or when you need an audit trail. Fine-tuning fits stable domains, strict output formats and latency-critical cases. Many production systems use both. See RAG vs fine-tuning for the trade-offs.
It depends on scale, hosting and whether you need filtering or relationships. Lightrains usually recommends ChromaDB for a prototype and Qdrant or Pinecone for production. The vector database comparison walks through the decision.
With an evaluation set of real questions and expected sources, scored separately for retrieval (did it find the right passages?) and generation (did the answer use them correctly?). The set is run on every change, so regressions show up before users see them.
Yes. Qdrant, Milvus and Weaviate can all be self-hosted, and the model can be an API or an open-weight model on your own hardware when data cannot leave your environment.
You do. All IP belongs to you, access to your systems is VPN-only under NDA, and payments are tied to milestones.
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