Start with Few-Shot Learning for Beginners
For teams new to AI, zero-shot (no examples) prompts are unreliable for complex tasks. Few-shot learning, where you provide 2-5 examples in the prompt, is a simple, highly-effective technique to guide the AI, improve accuracy, and make powerful AI tools more accessible to all developers.
Teams new to AI-assisted development should prioritize few-shot prompting patterns over zero-shot requests for any non-trivial task. Providing a few examples of the desired output (the "shots") trains the model on your specific context in real-time, dramatically improving its performance.
While large language models show remarkable zero-shot capabilities, they often fall short on complex tasks where a specific output format is needed. This leads directly to pain-point-03-hallucinated-capabilities (where the AI invents its own format) and pain-point-05-missing-context. Few-shot prompting is a form of in-context learning that steers the model to better performance by providing 2-5 demonstrations directly in the prompt. This technique is a crucial "on-ramp" for developers. It is highly efficient and flexible, offering substantial performance improvements without the high cost and complexity of fine-tuning an entire model. For teams working in established codebases, this is the most effective way to solve the pain-point-06-brownfield-penalty. You can "teach" the AI your team's specific coding patterns, API formats, or legacy structures by showing it examples, which enhances its adaptability and makes it a far more useful partner.
This recommendation should be applied whenever a zero-shot prompt provides inconsistent, incorrect, or poorly formatted results. It is the default approach for any task that requires a specific structure, style, or tone. This is especially true when asking the AI to perform tasks like: Refactoring code to a new pattern. Writing unit tests that must follow a specific team format. Generating code that interacts with a legacy or custom-built internal API.
Implementation is straightforward. Instead of just asking the AI to perform a task (zero-shot), structure your prompt to include examples. Start with a clear instruction. Provide 2-5 examples of the "input" and the corresponding "desired output." Provide your new "input" and ask the AI to generate the output. For example, instead of "Refactor this function to be a class," you would provide: Translate the following functions into the "WidgetService" class pattern. `` [My Output:] This guides the model to the exact structure you need, saving significant time.
Ready to implement this recommendation?
Explore our workflows and guardrails to learn how teams put this recommendation into practice.
Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.