Important knowledge is scattered
Review which documents should be available, who owns them and how their content can be extracted and kept current.
SERVICES / AI DEVELOPMENT & AUTOMATION / RAG DEVELOPMENT SERVICES
RAG Development Services.
I build retrieval-augmented generation applications that help users work with a defined collection of documents and knowledge. The goal is to find relevant material, show where an answer comes from and make the limits of the available evidence clear.
Discuss this serviceIDEA
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SYSTEM
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SYSTEM
01Inspect
02Retrieve
03Answer
04Evaluate
WHERE THIS HELPS
Useful answers begin with relevant, permitted sources.
A chatbot invents details instead of using your material
Design retrieval and answer behaviour around approved sources, with representative questions and explicit handling of missing evidence.
Different users should see different information
Apply the relevant access rules before source material is returned to the user or model, rather than rely on a prompt asking it to keep secrets.
THE APPROACH
A path from
problem to product.
Inspect
Review document formats, ownership, permissions, freshness and representative user questions.
Retrieve
Build ingestion and search, then check whether the right evidence is being retrieved.
Answer
Connect the model and source display with defined behaviour for weak or missing context.
Evaluate
Test answers, access boundaries and content changes, then document the update and operating process.
WHAT YOU GET
More than a
final screen.
We agree what is included before I start. These are the usual things you receive, based on your project.
PythonRAGLLM APIsEmbeddingsVector and keyword retrievalApplication APIs
- 01Knowledge-source and permission map
- 02Agreed document ingestion pipeline
- 03Retrieval and answer integration
- 04Source-reference presentation
- 05Representative evaluation set and findings
- 06Refresh, deletion and operating guidance
IN CONTEXT
Illustrative application — not a client case studyAn approved-document assistant
A user asks about an internal procedure, receives an answer linked to permitted source material and is told when the documents do not contain enough evidence.
Talk about a similar ideaUSEFUL TO KNOW
Before
we begin.
What is RAG in plain English?+
The application first finds relevant material from an approved source collection, then gives that context to a model to help answer the question.
Can it work with private company documents?+
It can be designed for private sources, but access rules, provider data handling and hosting choices must be reviewed. Private data should not be added before those boundaries are agreed.
Does RAG eliminate hallucinations?+
No. It can ground answers in retrieved evidence, but missing context, poor retrieval and model errors still need testing and handling.
Can users see the sources?+
Source references can be part of the interface so a user can inspect supporting material they are authorized to view.
How is this different from a chatbot project?+
RAG focuses on knowledge retrieval and evidence. A chatbot focuses on the conversation experience. A single product may need both, scoped together without duplicating the work.
BEYOND THE BRIEF
Begin with a knowledge collection you can maintain
A useful source set may include internal guidance, product documentation or approved support material. Before building, identify duplicate, outdated and contradictory documents. Scanned files, complex tables and poor source structure can add preparation work that should be visible in the scope.
BEYOND THE BRIEF
Test retrieval before polishing the answer
If the right evidence never reaches the model, a better-written response does not solve the problem. I examine retrieval and answer quality separately using questions based on real tasks. The test set includes questions the documents cannot answer and, where relevant, attempts to access material outside a user’s permissions.
BEYOND THE BRIEF
Knowledge changes after launch
The application needs a plan for new documents, corrections and deleted material. Permissions can also change. The scope defines how updates are processed and what users should expect while the index catches up. A one-time upload is not the same service as maintaining a live knowledge system.
BEYOND THE BRIEF
A bounded pilot can answer the important questions
Start with one approved document set and a focused user group. The estimate considers formats, volume, permissions, refresh requirements and evaluation depth. Running costs depend on storage, retrieval and model usage. I do not promise perfect answers or assume this system should make consequential decisions without review.
BEYOND THE BRIEF
Choose the knowledge sources for a RAG pilot
Identify the document collection, who may access it and how often its contents change. Provide non-sensitive sample questions, including one the documents cannot answer. These examples let us scope ingestion, retrieval, source references and the checks needed before the assistant is used with real material.