Help Needed: AI Automation in n8n Not Responding Accurately.

[Igor4]

I’m really struggling to build an automation in n8n that acts as an AI consulting a helpdesk. I’m using Olama with the models Qwen-2.5 14B and Qwen-3.0 14B, but it’s just not working—responses are inaccurate, and it fails to answer the questions. I’ve tried adding a bank dictionary, vectorizing the data, and using Qdrant, but I’m still stuck. I would really appreciate any kind advice or best practices. I’m honestly feeling a bit lost here, so any help would mean a lot.

[Haian_Abou-Karam]

Hi @Igor4 Welcome!!
I think (1) prompts, (2) few-shot examples, and (3) sampling settings like temperature, top_p definitely matter, but they usually do not fix the core problem by themselves.

For helpdesk-style RAG, the bigger issue is often retrieval quality and workflow design:
chunking
metadata
grounding
making sure the right context is passed in before the model answers.
In n8n, RAG is really about retrieving the right documents from a vector store first, then using the LLM on top of that.

So yes, I would look at prompt tuning and model parameters too, but I would focus first on the architecture and the quality of the retrieved context.
Read articles in:

Guides for Workflow Automation: Comprehensive Overviews - n8n Blog

for example:

RAG System Architecture: A Production Implementation Guide

[Igor4]

@Haian_Abou-Karam

@Haian_Abou-Karam

Hi Haian, thanks a lot for your answer, it helped me understand the problem better.

I have one more question about the architecture. Instead of giving the AI Agent access to the whole database or relying only on vectorized documents in Qdrant, would it be a good practice to create specific SQL views for the helpdesk use case and let the AI query only those views?

For example, views with already filtered and organized information such as customers, tickets, services, contracts, common issues, or internal knowledge records.

My idea is to avoid exposing the full database and give the AI a more controlled and structured source of truth. Do you think this approach would improve accuracy compared to giving broader database access or using only RAG with documents?

[Haian_Abou-Karam]

Hi @Igor4 , I’m glad my previous reply helped clarify things, and this is a very good follow up question.

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Yes, your idea of using SQL views is a strong approach. It gives the AI a more controlled and structured source of truth, instead of exposing the full database. In many helpdesk style use cases (customers, tickets, services), this can improve both accuracy and safety compared to broad DB access or relying only on RAG over documents.

From an architectural perspective, it depends on how your system is designed:

• If the data needed is deterministic, you already know what to fetch, calling the database first and then passing clean data to the AI is often better. It (1) reduces tool calls, (2) improves latency and cost, and (3) limits the chances of hallucination.
• If the system needs to decide dynamically what data to retrieve, then letting the AI Agent query those controlled views is a good pattern, but accuracy will depend on (1) prompt design, (2) tool descriptions, and (3) model behavior.

I see SQL views and RAG as complementary: views for structured operational data, RAG for unstructured knowledge.

In more complex scenarios, planning first patterns (like separating reasoning from tool execution), it is ReWOO (short for ‘Reasoning Without Observation’), can help, but they are usually needed only when workflows become more agentic.

Overall, your approach is solid and aligns well with building reliable systems.

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For further reading, IBM has a useful overview of agentic architecture and ReWOO.

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Happy workflow building