Hey there! In this tutorial, I'm going to show you how to create highly specialized AI agents by giving them access to your own fine-tuned models. Fine-tuning has been underused but is extremely powerful for many different use cases. Most people are intimidated by the setup, and most tutorials out there are code-based. But don't worry, I'll show you how to set up your own fine-tuned models without any code. Plus, I'll demonstrate how you can give your AI agents access to multiple specialized fine-tuned models, opening up a whole range of interesting use cases.
Why Fine-Tuning is a Game-Changer
Fine-tuning allows you to refine pre-trained AI models with specific data, making them specialized for particular tasks. This can significantly improve the accuracy and relevance of the model's output. For example, I recently delivered a content creation agent to a client that had access to five different fine-tuned models, each trained on the tone of voice and style of their different social media platforms like LinkedIn, X (formerly Twitter), Instagram, Facebook, and their blog. The results were amazing, both for me and the company.
Overview of the Content Team System
Before diving into the details, let's look at the content team system overview. This system will give you a good insight into the possibilities and use cases for this setup. The system is built on Relevance AI and consists of triggers, a content manager agent, and sub-agents with their specific tools.
Triggers
The system can either repurpose content based on long-form content like YouTube videos, podcasts, or blog posts, or it can generate posts from ideas. For example, someone could write an idea in a Slack channel, and the system will start generating posts for different platforms based on that idea.
Content Manager Agent
The content manager agent has three main responsibilities:
- Delegate Tasks: It delegates tasks to the right sub-agent.
- Report Back: It reports the generated content back to you for approval.
- Post Content: Once approved, it posts the content to the different platforms.
Sub-Agents
Each sub-agent is a specialist in a specific platform. For example, the LinkedIn writer agent has access to three tools:
- Fine-Tuned Model: Trained on all past LinkedIn posts of the company to mimic the style and tone.
- YouTube Transcription Tool: For repurposing content from YouTube videos.
- Audio Transcription Tool: For repurposing content from podcasts.
Practical Example: LinkedIn Post Writer Agent
Let's look at a practical example of a LinkedIn post writer agent. I recreated this agent for myself to show you the difference between a normal model output and a fine-tuned model output.
Normal Model Output
When I asked the normal model to write a LinkedIn post on "In the era of AI, English is the new programming language," the output was generic and didn't capture the tone I was looking for.
Fine-Tuned Model Output
The fine-tuned model, however, produced a much more engaging and relevant post. It understood the context and delivered the message in a style that matched my requirements.
What is Fine-Tuning?
Fine-tuning refines pre-trained AI models with specific data, making them specialized for particular tasks. This increases accuracy for those tasks but decreases accuracy for unrelated tasks. Fine-tuning adjusts the underlying language model, unlike a knowledge base that just adds context.
When to Use Fine-Tuning
- Setting Style and Tone: Perfect for brand-aligned or persona-aligned content.
- Improving Reliability: Ensures consistent output, like e-commerce product descriptions.
- Correcting Failures: Helps with complex prompts that normal models struggle with.
- Handling Edge Cases: Useful for specialized tasks like diagnosing rare medical conditions.
- Performing New Skills: Great for tasks hard to articulate in a prompt, like generating specific types of images.
Limitations of Fine-Tuning
- Overfitting: Models become overly specialized and struggle with generalization.
- Forgetting: Models quickly lose broad knowledge.
- Data Dependency: The quality and quantity of your data significantly impact the model's performance.
How to Fine-Tune Without Code
Step 1: Prepare Your Dataset
The hardest part is preparing your dataset. For LinkedIn posts, I set up a tool that scrapes LinkedIn posts and uploads them to a knowledge base in Relevance AI. You can then export this data into a CSV file.
Step 2: Convert to JSONL
Convert the CSV file to a JSONL file, which is the format required for training. You can use a tool like Novel Crafter to do this easily.
Step 3: Train Your Model
Upload the JSONL file to OpenAI's platform and start the fine-tuning process. This is free until the end of September, so take advantage of it!
Step 4: Integrate with Relevance AI
Once your model is fine-tuned, you can integrate it with Relevance AI. Use an API step to call your fine-tuned model, as Relevance AI doesn't support custom fine-tuned models directly.
Conclusion
Fine-tuning your AI models can significantly improve their performance for specific tasks. By giving your AI agents access to these specialized models, you can create highly optimized and brand-aligned content across multiple platforms. If you have any questions or need further assistance, feel free to join my community. Happy fine-tuning!
FAQs
How do I prepare my dataset for fine-tuning?
You can use tools to scrape data from platforms like LinkedIn and upload it to a knowledge base. Export this data into a CSV file and then convert it to JSONL format.
What is the difference between fine-tuning and using a knowledge base?
Fine-tuning adjusts the underlying language model, while a knowledge base adds context to the model without changing it.
How long does it take to fine-tune a model?
The time varies based on the size of your dataset. For a dataset of 100 data points, it may take around 20-30 minutes.
Can I use fine-tuned models for tasks other than content creation?
Yes, fine-tuned models can be used for various tasks like customer service, technical manuals, and diagnosing medical conditions.
What are the limitations of fine-tuning?
Fine-tuning can lead to overfitting, forgetting, and is highly dependent on the quality and quantity of your data.
