Haiku is the biggest release since GPT4 from a cost/performance perspective. Glad you got to dig into it, always enjoy your videos.

Thanks for the video! I would really love to see the follow-up video on function calling you mentioned.

My main use of Claude 3 currently is to have extended philosophical discussions with it, discuss texts and have it help me rewrite my own papers and drafts. I often begin the conversation with Opus to maximise quality. But when the context gets longer, I sometimes switch to Sonnet or Haiku. Haiku very often surprises me with how smart it is. When its responses are informed by the longer context, including the prior responses from Opus, this serves as something similar to a many-shot prompting method with explicit examples and it boosts Haiku's intelligence. Furthermore, Haiku's slightly more unfocused or meandering intellect enables it to make relevant connections between various parts of the conversation that Opus often misses due to its more focussed attention to user instructions and strict adherence to prompt. As a result of that, Haiku's responses sometimes are more intelligent, insightful and (broad) context sensitive even if it is slightly more prone to error than its bigger siblings.

Thanks, Sam. I had played with Haiku previously but have not done this optimised prompting. Jumping onto doing this now. Cheers.

Great overview and multimodal examples from the Anthropic Claude Cookbook using Haiku! I ran some of the examples multiple times with variations and the cost so far is less than a U.S. $1.00. We should definitely consider Haiku for personal and business apps where the tradeoff between quality and cost must be balanced: e.g. summarizing a large volume of papers and documents and creating and maintaining a large database of vector embeddings to support documents Q&A.

It seems to be that Haiku is a distilled / sparsified / quantized Opus. When it works, it gives results that are quite similar to Opus - while Sonnet gives very different results, so it looks it was trained independently. This is great: I often prep few-shot with Opus and then hand or over to Haiku for scale.

cant wait for the crewAI + Haiku video !!! it would be nice to have a superagent thats using OPUS and small agents only haiku.

Thank you for this. Quite helpful to me!

What a great model for local use. Thanks for showing it so clearly.

I had written off Haiku after testing my use case with it using the same prompt I use for opus/gpt-4. Totally unusable. After watching this, I revised the wording & format of the system prompt and added three examples. Well I'll be damned. Touché Haiku, touché. Not as nuanced and focused as opus/gpt-4, but definitely serviceable. The combination of the 200K context window and the pricing really is what makes this model special. Thanks for the informative video showing the proper way to leverage Haiku.

Haiku might be the perfect model to label / caption an image dataset at scale using natural language. Dalle-3's paper makes it clear that generating detailed natural language captions for each image was a big part of the magic behind its ability to understand and follow prompts so well at inference. SD3 only used a 50:50 mix of CogVLM-generated captions and captions from the original images. I think a Haiku-captioned training dataset would be a big step up for training these models.

Cheaper works for me as I'm in the learning/experimenting stage. Looking forward to you Claude 3 based function calling video. Thanks for sharing.

One of the big challenges I’m having is plugging haiku into all the places OpenAi APIs are accepted

So far wasn’t able to get anywhere with haiku for any production quality use case, but the idea with using many examples sounds promising. Will test out. Thx for inspiration to try again 😊

What a amazing explanation how to do with vision, xml and other stuffs with Haiku. Hopefully more in the future about Agents what you all mention with Crew AI. Many thnx.

Looking forward to your next video on CrewAI and Haiku

Impressive tutorial as always SAM I suggest you make a tutorial on how to build RAG system with agents using claude 3 haiku

I look forward to every one of these videos. Can you do more langchain or RAG examples with open source LLMs?

Thanks for this

Thanks for the video! Please Please, Function Calling using Haiku in Langchain

Can you post full course video about Claude 3.5 sonnet model

I hadn’t heard of the xml tag prompting with Claude before.

Please go over function calling asap really looking forward to it, from my test haiku is amazing with a few examples but still has some issues when I go upwards of 4 functions that can be called

DBRX just launched their new model on huggingface

great video - thank you

I think there is no function calling feature and for now only be used in a wrapper based applications.

I want to use claude haiku to process large documents...but end up running out of output tokens. Is there a simple hack (in langchain?...) to get a multipart response?

I’m not sure if it’s possible, but please use haiku with the more advanced crewai video that you mentioned making

can you share the link to the notebook you explained in the video ?

Out of curiosity tested all 3 models got dogs correct with 1st shot without prompting

The "fancy prompting" messing around with the count of dogs output is actually a glaring example of why all these models are crap. Lack of trustworthiness, making big mistakes on trivial tasks AND what's more, those mistakes depend on minute details of how you arrange the input! It reminds me of a fine tuned model claiming at the same time that it loves and hates tomatoes! Or claiming that its favorite animal is a tomato, unless, of course, you ask beforehand whether a tomato is an animal. This is simply ridiculous and inconsistencies of this sort highlight the simple fact that today even the most sophisticated of these models are still imitators of intelligence, rather than intelligence. Building castles on sand.

google bard is trash why was it rated so highly? it sucks at generative coding and variant annotation.

Claude 3 is actually better.