The author argues that these techniques are usually sufficient for most use cases, and fine-tuning should only be considered in cases with super stringent accuracy requirements, a need for fast edge inference, or if the combined use of few-shot and RAG do not provide the desired performance. The article concludes by recommending developers to start with the base LLM and the supplemental techniques before considering the resource-heavy process of fine-tuning.
Key takeaways:
- While fine-tuning a language model can make it more specialized for a particular task, it's often not necessary for building applications. Instead, techniques like few-shot prompting and retrieval-augmented generation (RAG) can be sufficient.
- Few-shot prompting involves providing examples of input/output pairs within the context window to guide the model's responses. This can help the model perform a specific task or output answers in a particular format.
- RAG allows the model to answer questions about content it hasn't been trained on by storing documents as embeddings in vector databases, which can be queried based on semantic meaning and passed into the base model prompt via the context window.
- While there are some cases where fine-tuning might be necessary, such as when there are stringent accuracy requirements, it's often more resource-efficient to use a base language model with the supplemental techniques mentioned.