When to use Prompt Chains. DITCHING LangChain. ALL HAIL Claude 3.5 Sonnet

From Prompts to Prompt Chains: When to Use Them and Why Startups Are Ditching Langchain

Are you curious about when to use prompt chains and why startups are moving away from Langchain and other LLM libraries? This video dives deep into these topics and reveals the minimalist prompt chaining method that can revolutionize your productivity.

🚀 In this video, we're breaking down the ULTIMATE guide to prompt chains using Claude 3.5 Sonnet, Anthropic's latest powerhouse LLM. Learn why startups are ditching complex libraries like Langchain and Autogen in favor of raw, unfiltered prompts.

🔥 Unlock the potential of minimalist prompt chaining and see how it can skyrocket your productivity. We'll show you:

1. A step-by-step breakdown of our minimalist chainable API

2. 4 crucial questions to determine when you should use prompt chains

3. The pitfalls of over-relying on LLM libraries and frameworks

💡 Discover why staying close to the metal with your prompts is CRITICAL in the ever-evolving AI landscape. We'll demonstrate how to build valuable prompt chains without unnecessary abstractions, giving you full control over your AI agents.

⚡️ Watch as we transform a simple factorial calculator into a powerful teaching tool using our minimalist approach. Plus, get a sneak peek at a production-level prompt chain driving a full agentic workflow!

🔧 Whether you're building AI coding assistants, research tools, or personal AI helpers, mastering prompt chains is your ticket to creating next-level agentic applications. Don't get left behind in the AI revolution!

🎓 Ready to level up your prompt engineering skills? Hit subscribe and join us on this journey to becoming an agentic engineering pro. Let's harness the true power of Claude 3.5 Sonnet and build AI agents that work tirelessly for you and your users.

💼 Remember, in the world of AI, the prompt is king. Don't give away your most valuable asset to complex libraries. Stay agile, stay close to the metal, and unlock the full potential of your AI workflows with prompt chains!

Like, subscribe, and comment with your thoughts on prompt chains and agentic workflows.

Let's COOK.

💻 Minimalist Prompt Chain Code

🔴 Master the prompt (Top 5 Elements)

🔗 Resources:

📖 Chapters
00:00 From Prompts to Prompt Chains
01:23 Minimalist Chainable API for Prompt Chains
04:00 Key Benefits of Using Prompt Chains
07:29 Four Guiding Questions for Using Prompt Chains
12:55 Problems with LLM Libraries like Langchain
15:52 Octomind blog post - Libraries are OVERKILL
18:20 Why the Prompt is All That Matters in Generative AI
19:22 Building a Production-Level Agentic Workflow
21:55 Closing Thoughts: Embrace Minimalism in AI Development

#anthropic #langchain #promptengineer

[Canna_Science_and_Technology]

I am anti black box solutions. I’ve written my agentic framework from scratch and the month of coding and headaches was worth it. I learned a ton and having full control over everything is priceless. It works so well, it’s almost scary. ;-)

[itskittyme]

i entirely agree with your take when you say it's too early to use these libraries,
because we are at the start of this new age of prompt engineering and these libraries force you to use patterns that have not been tested and engineered properly yet,
I can only recommend everyone to try things out yourself because you may stumble upon better patterns by trial and error, at this stage of the technology

[wellbishop]

Pure gold as always! Thank you so much.

[cutty]

Saving this til I get to work. I have been waiting to see if you release something about this :)

[jaredcluff]

I appreciate this. I am working on a new project that will leverage Mixture of Agents and was looking at which framework to use. I think you have convinced me to write and manage my own simple agentic framework instead.

[joshualunati]

Heay brudda, Just wanted to say that this has to be one of the most educational video about this subject, in quite some time. Thank you for your time.

[tomaszzielinski]

LangChain is, as name suggests, a promp chain framework. I write all the prompts from scratch, no idea where are you coming from. The only "library promp" I use is pydantic output formatting, which works flawlessly for various models.

Agree about too many levels of abstractions, but I do things in my own way, anyway. Ended up with very similiar function to what you show here.

[Dis-Trackted]

Cool video. I'll check the detailed implementation :)

[davieslacker]

Thanks for a very straight forward agentic workflow example without unnecessary libraries. This was what I have been looking for. I have felt like one could make a full time job simply studying langchain and seems like a level of abstraction of focus outside of the code. I want to dig straight into the models.

[asycd_]

Spot on about not needing Langchain but it gets you through the door. Especially, with the tricker methods to implement like LLM graphs!

[ScullyPopASMR]

This is so interesting. It's very complicated, but I will try to wrap my head around it.

[larsfaye]

I love that you use the SynthWave '84 theme with text glow. Best theme in existence!

[jackbauer]

I am pro black box solutions. As long as the results is acceptable regarding MY TASKS and criteria, nothing else really matters

[gheatza]

very interesting, thank you! I am a noobie that watches these kinds of videos from time to time to try and understand this tech and what I could build with them for myself.

setting that aside, I hope I'm not the weird person here but I really like the way your keyboard sounds 😆

[DeanRie]

Thanks for video, you are is super cool! 🙏

[ProzacgodAI]

Hey, I like this, it's more my speed. I had tooling that's basically the same as you have above. The biggest differences from what I can tell, in your prompts = [] ... instead of just raw strings I was using classes to describe the 'meta' around the prompt like JSON("generate blog post title about ...."), TEXT("generate one hook for ...."), these were just factory wrappers to a Meta(prompt, returnType, ctx) where ctx could also be set explicitly, I kinda rarely did that though. The second biggest was because I knew it was supposed to be json in the return, I could retry the prompt right in the chain lib, masking some % of errors so I didn't have to deal with them high level. I mean failure cases do get through, but failing to encode in json was generally not one of them.

[MavVRX]

Langchain is very customisable, and you can pick and choose what to use and what not to use. I was able to reduce a lot of unnecessary code when implementing a white paper for a different type of agent that uses a different parsing method.

[siegeperilous]

premature abstractions.. oops I abstracted. I swear that never happens. Lolol good shit. Clear. Simple. Powerful. Appreciated, brother.

[KhalilKhamlichi-ynwn]

I like your approach, can you make a tutorial also for DSPy ?

[DARKSXIDE]

dope work and insight i found the same issue w autogen, crew ai etc.