> Typed I/O for every LLM call. Use Pydantic. Define what goes in and out.

Sure, not related to DSPy though, and completely tablestakes. Also not sure why the whole article assumes the only language in the world is Python.

> Separate prompts from code. Forces you to think about prompts as distinct things.

There's really no reason prompts must live in a file with a .md or .json or .txt extension rather than .py/.ts/.go/.., except if you indeed work at a company that decided it's a good idea to let random people change prod runtime behavior. If someone can think of a scenario where this is actually a good idea, feel free to elighten me. I don't see how it's any more advisable than editing code in prod while it's running.

> Composable units. Every LLM call should be testable, mockable, chainable.

> Abstract model calls. Make swapping GPT-4 for Claude a one-line change.

And LiteLLM or `ai` (Vercel), the actually most used packages, aren't? You're comparing downloads with Langchain, probably the worst package to gain popularity of the last decade. It was just first to market, then after a short while most realized it's horrifically architected, and now it's just coasting on former name recognition while everyone who needs to get shit done uses something lighter like the above two.

> Eval infrastructure early. Day one. How will you know if a change helped?

Sure, to an extent. Outside of programming, most things where LLMs deliver actual value are very nondeterministic with no right answer. That's exactly what they offer. Plenty of which an LLM can't judge the quality of. Having basic evals is useful, but you can quickly run into their development taking more time than it's worth.

But above all.. the comments on this post immediately make clear that the biggest differentiator of DSPy is the prompt optimization. Yet this article doesn't mention that at all? Weird.

I conjecture that the core value proposition of DSPy is its optimizer? Yet the article doesn't really touch it in any important way. How does it work? How would I integrate it into my production? Is it even worth it for usual use-cases? Adding a retry is not a problem, creating and maintaining an AI control plane is. LangChain provides services for observability, online and offline evaluation, prompt engineering, deployment, you name it.

I think one thing that's lost in all of the LLM tooling is that it's LLM-or-nothing and people have lost knowledge of other ML approaches that actually work just fine, like entity recognition.

I understand it's easier to just throw every problem at an LLM but there are things where off-the-shelf ML/NLP products work just as well without the latency or expense.

This takes a ton of upfront work and careful thinking. As soon as you move the goalposts of what you're trying to achieve you also have to update the training and evaluation dataset to cover that new use case.

This can actually get in the way of moving fast. Often teams are not trying to optimize their prompts but even trying to figure out what the set of questions and right answers should be!

Also, IMO DSPy didn't take off because it requires preparing train and test datasets and that takes time and effort. Now with Gepa I expect things are getting very interesting, the optimizations can come just from descriptions.

IMO LangGraph is currently used a lot as an agent and RAG framework, DSPy doesn't have the same use case, even though there's overlap. And I think the montly numbers doesn't do justice, because what I see now is a lot of companies doing things wrongly.

Til about GEPA: https://github.com/gepa-ai/gepa

I think I might have just misunderstood how to use it.

I think a problem to DSPy is that they don't know the concept of THE WHOLE PRODUCT: https://en.wikipedia.org/wiki/Whole_product

Look at https://mastra.ai/ and https://www.copilotkit.ai/ to see how more inviting their pages look. A company is not selling only the product itself but all the other things around the product = THE WHOLE PRODUCT

A similar concept in developer tools is the docs are the product

Also I'm a fullstack javascript engineer and I don't use Python. Docs usually have a switch for the language at the top. Stripe.com is famous for it's docs and Developer Experience: https://docs.stripe.com/search#examples It's great to study other great products to get inspiration and copy the best traits that are relevant to your product as well.

The fact that you have to bundle input+output signatures and everything is dynamically typed (sometimes into the args) just make it annoying to use in codebases that have type annotations everywhere.

Plus their out of the box agent loop has been a joke for the longest time, and writing your own if feasible but it's night and day when trying to get something done with pydantic-ai.

Too bad because it has a lot of nice things, I wish it were more popular.

1. Up until about six months ago, modifying prompts by hand and incorporating terminology with very specific intent and observing edge cases and essentially directing the LLM in a direction to the intended outcome was somewhat meticulous and also somewhat tricky. This is what the industry was commonly referring to as prompt engineering.

2. With the current state of SOTA models like Opus 4.6, the agent that is developing my applications alongside of me often has a more intelligent and/or generalized view of the system that we're creating.

We've reached a point in the industry where smaller models can accomplish tasks that were reserved for only the largest models. And now that we use the most intelligent models to create those systems, the feedback loop which was patterned by DSPy has essentially become adopted as part of my development workflow.

I can write an agent and a prompt as a first pass using an agentic coder, and then based on the observation of the performance of the agent by my agentic coder, continue to iterate on my prompts until I arrive at satisfactory results. This is further supported by all of the documentation, specifications, data structures, and other I/O aspects of the application that the agent integrates in which the coding agent can take into account when constructing and evaluating agentic systems.

So DSPy was certainly onto something but the level of abstraction, at least in my personal use case has, moved up a layer instead of being integrated into the actual system.

1) It's slow: you first have to get acquainted with DSPY and then get hand-labeled data for prompt optimization. This can be a slow process so it's important to just label cases that are ambiguous, not obvious.

2) They know that manual prompt engineering is brittle, and want a prompt that's optimized and robust against a model they're invoking, which DSPy offers. However, it's really the optimizer (ex. GEPA) doing the heavy-lifting.

3) They don't actually want a model or prompt at all. They want a task completed, reliably, and they want that task to not regress in performance. Ideally, the task keeps improving in production.

Curious if folks in this thread feel more of these pains than the ones in the article.

So this article seems surprising since it emphasizes more the non prompt optimization aspects. If that was the selling point I would rather use something like Pydantic AI when I already use Pydantic for so much of the rest.

In my opinion, the reason people don't use DSPy is because DSPy aims to be a machine learning platform. And like the article says -- this feels different or hard to people who are not used to engineering with probabilistic outputs. But these days, many more people are programming with probability machines than ever before.

The absolute biggest time sink and 'here be dragons' of using LLMs is poke and hope prompt "engineering" without proper evaluation metrics.

> You don’t have to use DSPy. But you should build like someone who understands why it exists.

And this is the salient point, and I think it's very well stated. It's not about the framework per se, but about the methodology.

The real killer feature is the prompt compilation; it's also the hardest to get to an effective place and I frequently found myself needing more control over the context than it would allow. This was a while ago, so things may have improved. But good evals are hard and the really fancy algorithms will burn a lot of tokens to optimize your prompts.

Stranger still: it seems like every company I have worked with ends up building a half-baked version of Dspy.

Wrong. There can be a lot of subjectivity and pretending that some golden answer exists does more harm and narrows down the scope of what you can build.

My other main problem with data extraction tasks and why I'm not satisfied with any of the existing eval tools is that the schemas I write change can drastically as my understanding of the problem increases. And nothing really seems to handle that well, I mostly just resort to reading diffs of what happens when I change something and reading the input/output data very closely. Marimo is fantastic for anything visual like this btw.

Also there is a difference between: the problem in reality → the business model → your db/application schema → the schema you send to the LLM. And to actually improve your schema/prompt you have to be mindful of the entire problem stack and how you might separate things that are handled through post processing rather than by the LLM directly.

> Abstract model calls. Make swapping GPT-4 for Claude a one-line change.

And in practice random limitations like structured output API schema limits between providers can make this non-trivial. God I hate the Gemini API.

For many of the same reasons. A plethora of alteratives, personal preference, weird ideology, appropriateness for the task, inertia, not-invented-here.

The list goes on.

useful for upcoming consultants to learn how to price services too.

Edit, read the article -its really good- that cycle of AI engineering progression is spot on -read the article too!

Are we playing philosophy here? If you move some part of the code from the repo and into a database, then changing that database is still part of the deployment, but now you just made your versioning have identity crisis. Just put your prompts in your git repo and say no when someone requests an anti-pattern be implemented.

The only thing I'd grab dspy for at this point is to automate the edges of the agentic pipeline that could be improved with RL patterns. But if that is true, you're really shorting yourself by giving your domain DSPY. You should be building your own RL learning loops.

My experience: If you find yourself reaching for a tool like Dspy, you might be sitting on a scenario where reinforcement learning approaches would help even further up the stack than your prompts, and you're probably missing where the real optimization win is. (Think bigger)