<p>At one time, everyone rushed to finetuning, but it quickly became clear that it is expensive and complicated, requiring a quality dataset and serious resources. Finetuning is a last resort that should only be applied when necessary. This is why the step-by-step scheme from OpenAI is so valuable, allowing for excellent results by progressing from simple methods to more complex ones:</p>
<ol>
<li><strong>Prompt Engineering:</strong> the initial stage is optimizing queries. A simple prompt with clear instructions can already improve the result.</li>
<li><strong>Few-shot prompting:</strong> adding a few examples to increase the model's stability on similar queries.</li>
<li><strong>Retrieval-Augmented Generation (RAG):</strong> if specific knowledge is required, we add dynamic data loading into the prompt, allowing the model to work in the necessary context.</li>
<li><strong>Fine-tuning:</strong> the final step when absolute accuracy and stability are needed. Here, a specialized dataset is created to train the model in real conditions.</li>
</ol>
<p>I would also add the idea of multi-agent systems and the simultaneous use of models from different providers, but it’s clear that we shouldn’t expect this in OpenAI’s documentation.</p>
<p>📝 More details: <a href="https://platform.openai.com/docs/guides/optimizing-llm-accuracy">https://platform.openai.com/docs/guides/optimizing-llm-accuracy</a></p>;
<p>#ai #openai #coding #llm #prompting #finetunning #rag</p>
