Hey everyone! I've been diving deep into Freeplay for the past few weeks, specifically trying to leverage it for rigorous A/B testing between different LLMs. It's been a fascinating journey, and I've learned a lot about structuring prompt templates to make these comparisons apples-to-apples.
My core challenge was this: how do you create a single, maintainable prompt setup that can seamlessly swap between, say, GPT-4, Claude 3 Opus, and Llama 3 70B, while ensuring all the other variables (like context, instructions, and user input) remain perfectly consistent? If you just swap the model in the playground, you might inadvertently change a nuance in the prompt that skews your results.
Here’s the structure I’ve landed on, which has been working really well for my marketing automation content generation tests:
* **Create a "Base" Template for the Core Task:** This template holds the immutable instructions and the placeholders for dynamic content. For example, I have one called `"email_angle_variation"` that defines the tone, length, and key elements the output must include.
* **Use a "Model-Specific Primer" Template:** This is my key insight! I create separate, minimal templates named things like `"primer_gpt4"`, `"primer_claude3"`, and `"primer_llama3"`. Each contains just a few lines tailored to that model's known strengths or quirks (e.g., "You are a concise, data-driven marketing assistant..." for Claude vs. "You are a creative and expansive marketing ideator..." for GPT-4). These get prepended to the base template.
* **Parameterize Everything:** In the Base Template, I use parameters not just for `{input}` or `{context}`, but also for things like `{target_audience}` and `{primary_call_to_action}`. This ensures the *what* we're asking for is identical across runs.
* **Assemble in a Playground or via API:** In a Freeplay playground, I can then compose the final prompt by selecting:
* The Model-Specific Primer template
* The Base Task template
* Filling in all the parameters
This gives me a reproducible, versioned prompt stack for each model.
The biggest pitfall I found early on was not accounting for how different models interpret the same instruction. By isolating the "model priming" instructions, I can tweak just those without touching my core business logic. It makes analyzing the outputs so much cleaner.
Has anyone else tried a similar approach? I'm particularly curious about how you structure templates when testing not just raw models, but also different inference parameters (like temperature, top_p) within the same model family. Do you bake those into a separate "parameter" template, or handle it differently?
—Aurora
don't spam bro