Fusion Chain - NEED the BEST Prompt Results at ANY COST? Watch this…
AI Summary
Summary: The Prompt and Prompt Chaining in Large Language Models (LLMs)
- The Prompt
- Fundamental unit of knowledge work.
- Initiates the use of LLMs for computation.
- Prompt Chaining
- Linking multiple prompts sequentially.
- Output of one prompt becomes input for the next.
- Can reference outputs from any previous prompts.
- Enhances reasoning abilities of LLMs.
- Reflects the sequential nature of daily tasks.
- Agentic Workflows
- Workflows with a sequence of tasks.
- Can be non-linear and involve multi-agent systems.
- Important for building applications and products.
- Fusion Chain
- Multiplying prompt chains across different models.
- Combines outputs from various models to get the best result.
- Includes an evaluator to decide the best fit.
- Can be a prompt call or a chain itself.
- Performance and Relevance of Prompt Chains
- Adding multiple chains improves performance.
- Newer models like GPT-5, CLA-4, and Gemini 2 may outperform older prompt chains.
- Prompt chains remain a critical abstraction for maximizing model capabilities.
- Optimal Flow of Prompts
- No definitive answer for the optimal flow.
- Case-specific and depends on the problem being solved.
- Using a prompt chain is better than a single prompt.
- Fusion Chain API
- Recommended to stay close to the metal, avoiding reliance on libraries.
- Example provided with a simple prompt chain and a fusion chain.
- Agentic Workflow Example
- Zero Noise application scrapes websites for updates.
- Uses fusion chain to determine best HTML selectors.
- Workflow includes retrieval, LLM processing, and action based on information.
- Conclusion
- Prompt chaining and fusion chains are powerful tools for building advanced AI-driven workflows.
- The optimal use of these abstractions is still being explored.
For more detailed information, refer to the provided examples and discussions in the video.