Three Ways to Optimize LLMs like ChatGPT & Gemini in Odoo

Three Ways to Optimize LLMs like ChatGPT & Gemini in Odoo

Author: Kerrim Abd El Hamed

In our recent blog posts, we explored how LLMs like ChatGPT and Gemini can automate Odoo documentation and generate insightful task descriptions. Now, let’s delve deeper into the technical nuances of successful LLM integration within Odoo. At MuK IT, we’ve identified three essential techniques for crafting truly powerful and effective AI-driven Odoo solutions.

1. Precision Prompting:

It’s not enough to simply “ask” a LLM a question. We need to craft precise, context-rich prompts that guide the AI towards the desired outcome. Here’s a code example from us extending Odoo’s project management module that demonstrates how we build effective prompts:

for record in self:
    body = "Please solve the task and print all necessary files in your response."
    # ...
    if record.description:
        body += "\nThe task description says:\n" + record.description
    # ...
    message_id = record.message_post(body=body, subject='Question for Task [%i] %s' % (,, process_with_gemini=True) 

This snippet demonstrates how we don’t simply ask the LLM to “solve the task,” but provide crucial context by including fields from Odoo like the task description, guiding the LLM towards a more relevant solution.

Effective prompt design also involves:

  • Specificity: Clearly stating the desired action, format, and any constraints.
  • Context: Providing background information, such as project details or Odoo data points.
  • Examples: Including examples of the desired output style within the prompt.

2. Maintaining Context:

LLMs often lack memory of past interactions. To overcome this, we build context management systems within Odoo to:

  • Log Conversation History: Retaining a record of past user-LLM exchanges.
  • Enrich Prompts with History: Including relevant snippets from previous interactions in new requests.
  • Manage Context Limits: Summarizing or prioritizing conversation history to accommodate LLM limitations.

3. Fine-Tuning:

General-purpose LLMs benefit from specialized training on your unique Odoo data. This process involves:

  • Curating Data: Building a dataset from your Odoo documentation, CRM data, and internal communications.
  • Model Specialization: Using this dataset to fine-tune the LLM, aligning its outputs with your Odoo environment.
  • Integration: Implementing the fine-tuned LLM back into your Odoo system for enhanced accuracy and relevance.

MuK IT: Your Partner in AI-Powered Odoo Solutions

Contact us today to explore how our expertise in LLM integration can elevate your Odoo experience.