When your RAG chatbot gives wrong or made-up answers, the cause is usually in retrieval, not the model: poor chunking, weak embeddings or no reranking. We evaluate your pipeline, fix chunking and retrieval, add reranking where needed and show you before/after results on your own questions.
Is this your problem?
Your RAG chatbot gives wrong or made-up answers, even when the answer is in your documents.
It ignores the most relevant document and quotes something unrelated.
Answer quality is inconsistent — good one day, wrong the next.
Users have stopped trusting it, and you cannot tell whether the problem is the model, the data or the retrieval.
Why this happens
In most RAG systems, bad answers start before the model is called. Documents are split into chunks that cut tables and sections in half, the embedding model does not match your content or language, top-k retrieval returns near-misses with no reranking, and nobody has a test set to measure whether a change made things better or worse. The LLM then answers confidently from the wrong context.
What we do
Build an evaluation set from your real questions and expected answers.
Measure the baseline — retrieval hit rate and answer accuracy.
Fix chunking so sections, tables and metadata stay intact.
Fix retrieval — embedding model, hybrid search, filters and top-k.
Add reranking and grounding prompts where they improve results.
Re-test against the same evaluation set and report the difference.
What you get
Improved RAG pipeline committed to your repository
Before/after evaluation on your own questions
Change notes explaining what was fixed and why
The evaluation set, so you can re-test after future changes
What we need from you
Access to the repository or RAG pipeline
15–30 sample questions with the answers you expect
Access to the source documents or vector store
Common cases we handle
LangChain and LlamaIndex pipelines
Vector stores such as Pinecone, Weaviate, Qdrant, Chroma and pgvector
OpenAI, Claude, Gemini and open-source models
Internal knowledge bases, support bots and chat-with-PDF features
Pricing and turnaround
Starting price | From $99 |
Delivery | Typically 48h |
Priority delivery | 12h for +50% |
Includes | Scope check, the work, deliverables and handover notes |
Every task gets a fixed price, confirmed after the free scope check. Your code and data are used only for this task, and we sign an NDA on request.
How it works
Submit your task. Tell us what you need and share the files or access listed above.
Free 30-minute scope check. An engineer confirms the scope and gives you a fixed price before any work starts.
We do the work. Your task is handled by Codersarts' own engineering team — not a freelancer marketplace.
Delivery and handover. You get the deliverables, a walkthrough of what changed, and time to ask questions.
Related tasks
Integrate OpenAI or Claude API — to add or rebuild the LLM layer behind your chatbot.
Build AI Agent Workflow — to turn your chatbot into an agent that takes action.
Clean & Prepare Dataset — if messy source data is feeding bad answers.
Need more than a single task?
If you need a production RAG system designed and built end to end, see Codersarts Build Solutions.
FAQ
How much does it cost to fix a RAG chatbot?
From $99 for a Standard Task. The fixed price is confirmed after a free 30-minute scope check.
How long does it take?
Typically 48 hours. Priority 12-hour delivery is available for +50%.
How do I know the fix worked?
We measure answer accuracy on your own questions before and after the changes and share both results.
Do I need to switch LLM providers?
Usually not. Most quality problems come from chunking and retrieval, so we fix those first and only recommend a model change if the evaluation shows it helps.
Is my data confidential?
Yes. Access is used only for this task, and we sign an NDA on request.
Prefer to learn it yourself? Explore hands-on courses at Codersarts Labs.