Part 9/11:
- Answering: Use an LLM to read, summarize, and synthesize relevant data.
This method is scalable and cost-efficient, making it ideal for large corpora.
Practical Implications and Recommendations
For Question Answering (QA):
Instead of fine-tuning for QA tasks, semantic search combined with LLMs offers a better, faster, and more reliable solution.
Fine-tuned models can assist in specific task patterns, but for knowledge retrieval, prefer vector-based search.
For Building Knowledge Bases:
Do not rely solely on fine-tuning.
Implement semantic embeddings and retrieval mechanisms.
Fine-tuning may be useful for pattern-specific tasks (e.g., formatting responses, code generation).