Introduction to ChainBuddy and the “Blank Page Problem”
Explore the “blank page problem” in LLM evaluation and understand how the ChainBuddy agent system is designed to solve it.
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Problem space: LLM evaluation
Before we consider the agentic solution, it’s crucial to understand the design challenge we’re trying to solve. In this chapter, we’ll follow a real-world journey: turning a vague research question into a robust, automated LLM evaluation pipeline.
The core of the problem lies in the difficulty of rigorously testing and comparing LLM behavior. Let’s suppose that we are AI developers to see why this is such a common and complex challenge.
Understanding the challenge in LLM evaluation
Imagine that we are tasked with answering what appears to be a simple question.
Question: “Does an LLM provide a better solution to a complex math problem if we prompt or instruct it to act like a ‘mathematician’ vs. a ‘high school student’?”
To answer this properly, we need to frame it as a clear experiment. Our independent variable (the “knob” we’re turning) is the persona given to the LLM. Our dependent variable (the outcome we’re measuring) is the accuracy of its answer.
By isolating these variables, we can design a robust test. However, to actually run this test, we need to build the entire pipeline. This is where a powerful LLM evaluation tool like ChainForge comes in.
ChainForge is an open-source, visual toolkit for prompt engineering and LLM evaluation. It’s designed to help developers, researchers, and product teams systematically test, compare, and analyze large language model (LLM) behavior without having to write complex code. Think of it as a “playground + lab” for working with multiple LLMs in a structured and visual way.
We open the tool, but we’re greeted with a blank canvas. Now, the responsibility is on us to construct the pipeline from scratch. What does that involve?
Create personas: To be thorough, we can’t just test two. We would need to create a list of a dozen different roles (e.g., ‘physicist,’ ‘historian,’ ‘poet’).
Write the prompts: We’d need a prompt template to combine our variables, like
As a {persona}, solve the following: {math_question}.Select the models: Is this effect unique to one model? We’ll have to test it across several top models, like GPT-4, Gemini, and Claude.
Evaluate the results: With dozens of responses, manually checking them is infeasible. We would need to write a code evaluator, a program that automatically parses each response and checks for the correct answer.
Visualize the outcome: Finally, we’d need to create a chart to clearly see which persona-model combination performed best. ...