Changelog Master Feed Collaboration & evaluation for LLM apps (Practical AI #253)
Jan 23, 2024
Explore the challenges and importance of collaboration in building AI-driven apps, focusing on prompt iteration, versioning, management, evaluation, and monitoring. Learn how Humanloop aids in managing different prompt versions and model configurations. Discover the benefits of integrating closed and open models in workflows. Dive into the use of Human Loop in building question answering systems and the exciting future of AI advancements.
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Prompt Engineering as Code
- Prompt engineering enables non-technical domain experts to customize LLM applications effectively.
- Prompts require rigorous versioning and collaboration akin to software code to ensure reliability.
Prioritize Prompt Engineering First
- Start by pushing prompt engineering to refine your LLM's behavior before considering costly fine tuning.
- Fine tuning suits use cases needing latency, cost optimization, or consistent output format.
Retrieval Over Fine Tuning
- Retrieval-augmented generation often outperforms fine tuning for incorporating private or up-to-date data to LLMs.
- Fine tuning is less common because prompt engineering plus retrieval is surprisingly effective and easier to iterate.