Best practices for fine tuning and prompt engineering LLM

Description

The Fine-Tuning Landscape

• Full Fine-Tuning: Update all model layers for domain-specific adaptation.
• Parameter-Efficient Fine-Tuning: Methods like LoRA, Adapter-Tuning, and Prefix-Tuning offer faster training with fewer resources.
• Instruction-Tuning & RLHF: Align models with human-like reasoning through Reinforcement Learning with Human Feedback.

Prompt Engineering Simplified
Startups can gain quick wins with API-first approaches and strategic prompt design:

• Prompt Mining & Paraphrasing: Generate optimized prompts for varied inputs.
• Chain of Thought Prompting: Break down complex tasks for step-by-step accuracy.
• Prompt Chaining: Link tasks to create comprehensive workflows (integrate tools like LangChain).

Strategic Roadmap for Startups

1. Start with API-based models to avoid upfront complexity.
2. Use prompt engineering to maximize out-of-the-box model performance.
3. Gradually implement fine-tuning for more customized solutions as scale demands.