Principle:FMInference FlexLLMGen Input Prompt Design
| Field | Value |
|---|---|
| Sources | FlexLLMGen, GPT-3 |
| Domains | Prompt_Engineering, NLP |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A prompt engineering pattern for constructing structured input prompts that guide language models to perform specific tasks like question answering and information extraction.
Description
Effective use of language models for tasks like Q&A and entity extraction requires carefully structured prompts. The pattern involves: (1) providing a few labeled examples (few-shot demonstrations) that establish the expected input-output format, (2) formatting the query in the same pattern as the demonstrations, and (3) leaving the answer position open for the model to complete. FlexLLMGen's completion example demonstrates this with Q&A prompts (Question/Answer format) and extraction prompts (Text/Airport codes format). The prompts are constructed as Python string lists and passed directly to the tokenizer.
Usage
Design prompts following the demonstrated pattern when using FlexLLMGen for text completion tasks. Match the prompt format to the target task (Q&A, extraction, classification, etc.).
Theoretical Basis
Few-shot prompting leverages in-context learning: by showing the model labeled examples in the prompt, it learns the task format without parameter updates. Consistent formatting between demonstrations and the query is critical for reliable generation.