LLM Engineering / RAG Systems
RAG System Development
Retrieval-augmented generation lets an LLM answer from your documents, knowledge base, and data instead of guessing. We build RAG systems with the right ingestion, chunking, retrieval, permissions, and evaluation, so answers are accurate, sourced, and safe to show users.
This Is For You If...
You want a chatbot or assistant that answers from your documents and data
Your RAG prototype gives vague, wrong, or made-up answers
Users must only see answers from documents they're allowed to access
You need sources shown with every answer
Which Area Matches Your Need?
Ask Yourself | Points Toward |
Are you starting a new RAG system? | RAG Architecture |
Is the source data messy or varied? | Ingestion & Chunking |
Are answers wrong or unsupported? | Retrieval & Answer Quality |
Do different users see different data? | Permissions & Security |
RAG Services We Offer
RAG Architecture
Choosing models, embeddings, vector store, and orchestration for your data, scale, and budget.
Ingestion & Chunking
Parsing PDFs, web pages, docs, and databases into clean, well-structured chunks with metadata, kept in sync as sources change.
Retrieval & Answer Quality
Hybrid search, re-ranking, query rewriting, and prompts that cite sources and say "I don't know" when the data doesn't answer.
Permissions & Security
Document-level access control applied at retrieval, plus protection against prompt injection from retrieved content.
What We Need From You
What to Share | Why It Helps |
The source documents and data | Defines ingestion work |
Example questions and good answers | Used to evaluate quality |
Who can see what | Shapes permission design |
Where the assistant will live | Web app, Slack, or internal tool |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Chunking by fixed size only | Splits meaning and loses context | Chunk by document structure |
Blaming the model for bad answers | Retrieval is usually the cause | Evaluate retrieval separately |
No permission filtering | Users see restricted content | Filter at retrieval time |
No evaluation set | Quality can't be measured | Track answers against expected results |
How It Works
1. Gather sources and questions
Data plus real questions
2. Build ingestion
Parsing, chunking, and indexing
3. Build retrieval and answers
With citations and permissions
4. Evaluate
Retrieval and answer quality measured
5. Deploy
With monitoring and feedback capture
Why Codersarts
We measure retrieval and answer quality separately, so we know exactly what to fix when answers go wrong — and every answer can point back to its source.
Turnaround Time
A working RAG system on a defined document set is typically delivered within two to three weeks. Large or multi-source systems are scoped individually.
FAQ
Which vector database do you use?
Chosen for your needs — see Vector Databases.
Can it work with private data securely?
Yes, including private hosting and models where required.
Can you fix our existing RAG system?
Yes — evaluation usually shows quickly where it's failing.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Want AI Answers You Can Trust?
Build a RAG system grounded in your data, with sources.