What exactly is DSPy? You may find its GitHub page filled with technical details that can be overwhelming, so here’s a simplified breakdown.

DSPy allows you to train your AI agent using self-learning techniques. But how does it work? Let’s start by understanding what we mean by an AI "Agent."

An AI agent is essentially a system that can autonomously handle tasks like database operations, API calls, and interactions with large language models (LLMs). After gathering data, the agent formats it into natural language to deliver a seamless experience to the user.

When calling an LLM, prompt engineering becomes essential. This involves carefully crafting prompts to yield useful responses. However, as models evolve, previously effective prompts might not work as intended.

Here's where DSPy steps in.

With DSPy, you define a basic template that calls the LLM with your query. Imagine you’re using a relatively basic LLM prone to inaccuracies. DSPy can also involve a more powerful model, like OpenAI’s, to assess and correct any mistakes made by the primary LLM. Based on this feedback, a numerical score is assigned, allowing the agent to adjust and learn from its performance.

Through repeated feedback cycles, your agent gradually becomes more accurate and requires fewer prompt adjustments, ultimately refining its internal prompts. Over time, even with a less powerful model, it can produce consistently reliable outputs.

This concept opens up a world of possibilities, such as using it to develop an AI Site Reliability Engineer (SRE), to name one exciting application.