Make GenAI models do what you want
As GenAI startups strive to unlock cutting-edge potential, understanding fine-tuning and prompt engineering becomes crucial. These techniques optimize model performance, align outputs with specific goals, and help scale innovation effectively.
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
- Start with API-based models to avoid upfront complexity.
- Use prompt engineering to maximize out-of-the-box model performance.
- Gradually implement fine-tuning for more customized solutions as scale demands.
Whether you’re exploring DPO or leveraging gradient-based prompt design, these strategies are essential for navigating the GenAI revolution. Watch the video for a deeper dive into these transformative techniques!
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Best practices for fine tuning and prompt engineering LLM
Download the white paper from Wandb that go over best practices on fine tuning and prompt engineering.
