Enterprise RAG Development

Make company knowledge usable

Product manuals, internal procedures and support documents often contain the answer a user needs, but locating the right passage takes time. A RAG assistant retrieves that evidence before generating an answer, giving users a way to inspect where the response came from.

This service fits technical support, internal knowledge search and evidence-backed research workflows. My Agentic RAG Support Assistant demonstrates the document-to-answer pattern for software product questions. A successful deployment still depends on the quality, freshness and permitted use of the customer's own material.

Inputs, outputs and scope

Inputs

Authorized documents or knowledge APIs, source ownership, document versions, user access rules and real questions with expected evidence.

Outputs

An ingestion pipeline, searchable index, retrieval and answer API, source-linked responses, evaluation cases and update procedures.

Decisions to agree

What content is in scope, who may retrieve it, how updates reach the index and how an unsupported question should be handled.

Technical approach

1. Prepare documents with provenance

   Extract usable text and preserve document identity, section context, version and source location. Chunk around meaningful boundaries so retrieval returns enough context to support an answer.

2. Test retrieval separately

   Choose embeddings and a vector index for the content and operational requirements. Compare retrieved passages against a curated question set before evaluating generated answers. Metadata filters and additional keyword retrieval can be evaluated where the corpus needs them.

3. Generate answers from evidence

   Build prompts around the retrieved passages and return source references alongside the answer. Keep instructions separate from document content and provide an explicit response when supporting evidence is missing.

4. Integrate access and updates

   Apply user permissions before presenting retrieved content. Define document refresh, deletion and re-indexing behavior so stale or removed documents do not silently remain part of the knowledge source.

Delivery and evaluation

A first milestone uses a small, representative document set and a collection of real questions. It establishes whether the required evidence is retrievable before adding chat history, agents or a larger interface. The next milestone integrates the application API and document update process.

Evaluation separates retrieval relevance from answer quality. We inspect source coverage, citation correctness, unsupported assertions, missing-answer behavior, latency and cost. The handover includes ingestion instructions, index configuration, evaluation examples and a way to investigate an answer using its source passages.

Relevant public work

These project pages provide concrete examples of related engineering and identify prototype or research scope where applicable.

Questions about this service

Should we fine-tune a model instead of building RAG?

That depends on the problem. RAG is useful when answers need current, inspectable source knowledge. Fine-tuning can address different behavior or formatting needs. I would first test whether retrieval over your documents solves the actual task.

Can the assistant keep different teams' documents separate?

Access requirements should be part of the data and retrieval design from the start. Results must be filtered according to the requesting user's permissions, with tests for content that user is not allowed to see.

What happens when a document changes?

The pipeline needs a defined update process that refreshes affected content and removes superseded material. Source identifiers and versions help explain which document supported an answer.