2 Executing prompts programmatically
This chapter covers
- Understanding prompts and prompt engineering
- Different kinds of prompts and how they're structured
- Enhancing prompt responses using one, two, or a few-shot learning
- Examples of using prompts with ChatGPT and the OpenAI API
Applications built with LangChain interact with LLMs primarily through prompts—structured inputs that guide the model’s behavior. Think of it as giving instructions to a highly capable but inexperienced colleague: the clearer and more specific your instructions, the better the outcome. To generate accurate and relevant responses, your prompts must be well-crafted and tailored to the task at hand. In LangChain, prompt design plays a central role in determining how your application performs.
Prompt engineering—the practice of designing and refining prompts to steer the LLM’s output—is a core skill in LLM application development. You’ll spend a significant amount of time creating, testing, and iterating on prompts to ensure your system consistently delivers high-quality results. LangChain recognizes this and places prompt engineering at the heart of its design, offering a suite of tools to support the process. Whether you're writing simple instructions or building sophisticated templates that require advanced reasoning and dynamic input handling, LangChain provides the infrastructure to manage and scale your prompt-driven workflows effectively.