Fusion Chain: NEED the BEST Prompt Results at ANY COST? Watch this…

In-depth rational breakdowns are missing in this space… Dan definitely is one of the best AI channels on YouTube.. worthy sub too because I wanna see this channel grow

It would be interesting to explore the evaluator function in depth. Like it’s one thing if you have a hard defined set of criteria for what a good end respons is. It’s another thing when the definition of a good response can be more less easy to define. Like the advice of an AI therapist. If you have three chains you’re getting three slightly different answers and there in general very similar but in that case how do you determine the best answer? My thinking is that in that case it’s really important to understand the end user, in order to be able to accurately choose the best response to deliver them. So it’s the idea that the current mental and emotional state of the end user (patient) and their goals help make the decision of what response that evaluator will choose. It’s the idea that one of those responses will get that patient closer to their goals factoring in their current emotional and mental state. So it would definitely need to probably send the responses from the different chains and the patient data to another set of chains which attempt to simulate the future or predict make predictions.

8:10 U could develop system of having the best prompts chosen by an initiator agent and an evaluator agent. Have the initiator agent built into a reinforcement algorithm that's been trained or tuned with a related dataset. Then have both agents run in a feedback loop until the optimal prompt flows is chosen. Have the evaluator use some kind of metrics or benchmarks to rate the output, until the most optimized output is chosen. (This also might put prompt engineers, out of work if it ever became a thing)

Like MoA with prompt chains!

Great implementation of Fusion Chain. Thanks

AWESOME! Tks

Great vid as always, thanks! Also very interested in Zero Noise. Let me know when you Open Source that. Looks very interesting!

And are on, our internet -- i.e. simulated device net!

I think your fusion is missing a key step in between where each LLM gets a revision step and access to all answers to revise their previous answer. Add that in before the final resolver and you have gold. I can link a demo.

You always good solutions but with very high and costly implementations. Why not use Local LLMs?

what about the cost?

interesting, would love to see this added as an option to Aider. Not sure how one can say this would outperform gpt-5 without trying it, but using gpt-5 in a fusion chain makes sense. Of course though, there's the cost... once the end result is funnelled back to the start does the whole chain run again?

How might we fold DSPY into this concept. Stanford storm employs DSPY. Storm takes prompt chains, so DSPY takes prompt chains. Is there a better workflow?

What you think about langgraph to build agentic workflows??

Could I use Flowise to accomplish a fusion chain?

Vst am int -- Oriental where, he used, policy Dutch Waffen! V=verified -- hence; I served - with them! Hence; I -- had - that int! Fused index! You have, no f(x) - and call it, any int!

Gpt5? And clode 4? Not 4 and 3.5?

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