Slides: DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners

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DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners

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Extracted Slides

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OTT D) iS a

DSPy is a declarative framework for building modular AI software.

It allows you to iterate fast on structured code, rather than brittle strings, and

offers algorithms that compile AI programs into effective prompts and

weights for your language models

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Use Cases Lighting Round

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- Simple sentiment classifier Fl El

- Structured information from a PDF = 1

- Multimodal extraction [a]:

- Web research agent (using Tools) .

- Detect boundaries of a document github.com/kmad/aie

- Recursively summarize an arbitrary-length document

- GEPA example

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DSPy allows you to decompose logic into a -

program that treats LLMs as a first class citizen...

... Without having to tweak prompts (unless you

want to)

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detailed control over your program while focusing on

things that actually matter

e Allows you to create computer programs that use LLMs

as inline function calls

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Mi iN" | mM S Tt ff h © Programs which you happen to be able to optimize - it's a

programming paradigm, not a wholesale framework, and not

qa n qa 1 0 C ALU “optimizer-first”

e Is built with a systems mindset; you encode intent and

structure in a way that is transferable

© Your program design likely moves slower than Al advancements (at

least so far)

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this way of working

found it useful - the hope is to

tives for you to extrapolate to

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Core Concepts

Specify what you want, Structure your program Interact with the outside

not How: let the LIM logically world - or the rest of the

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Adapters D

Customizable prompt Optimize your DSPy Define what to optimize

formatters: think JSON. Pyke eter en YO enleu(a against (can be multiple

BAML., XML. ete. things)

(let the LLM figure it out!)

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e How you “express your

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complex Class-based objects

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input text to classify sentiment

he more positive

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input text to classify sentiment

he more positive

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fetes oe Fe sale of common nteck shares on twe datesiunins On P1/2006, 209, 200 shares were

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Optimizers

| DSPy has various built-in primitives that allow you to then optimize your

program. This allows you to quantitatively improve your performance and

cost profile.

_ “A DSPy optimizer is an algorithm that can tune the parameters of a DSPy

| program (i.e., the prompts and/or the LM weights) to maximize the metrics

you specify, like accuracy.”

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“DSPy is not an optimizer. It's set of

programming abstractions (signatures, modules)

that can be optimized.”

- Omar Khattab @lateinteraction

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The reason that this is tricky is quite subtle. It’s the fact that \

anytime you use an LLM to assign a reward, those LLMs are giant

things with billions of parameters, and they're gameable. If you're

reinforcement learning with respect to them, you will find - Andrej Karpathy

adversarial examples for your LLM judges, almost guaranteed. (via the Dwarkesh

So you can’t do this for too long. You do maybe 10 steps or 20 Podeast)

steps, and maybe it will work, but you can’t do 100 or 1,000. I

understand it’s not obvious, but basically the model will find little

cracks. It will find all these spurious things in the nooks and

crannies of the giant model and find a way to cheat it

@

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GEPA: REFLECTIVE PROMPT EVOLUTION CAN OUTPERFORM = = ra

REINFORCEMENT LEARNING ae

Lakshya A Agrawal’, Shangyin Tan’, Dilara Soylu’, Noah Ziems‘, Ls 7

Rishi Khare!. Krista Opsahl-Ong', Amay Singhvi?*, Herumb Shandilya’.

Michael J Ryan’, Meng Jiang‘. Christopher Potts’. Koushik Sen’. ia

Alexandros G. Dimakis'-', lon Stoica', Dan Klein', Matei Zaharia'’, Omar Khattab® .

"UC Berkeley "Stanford University BespokeLabs.ai ‘Notre Dame ‘“Databricks = MIT Chris Potts

https://www.youtube.com/

watch?v=Obkwd90 Yaqfk

“Model —HotpotQA_IFBench Hover PUPA Aggregate Improvement

Qwen3-8B

Baseline 42.33 36.90 35.33 80.82 48.85 —

MIPROv?2 6 47. 81 1] 6.26

GRPO 43.33 35.88 38.67 86.66 51.14 +2.29

GEPA 62.33 38.61 52.33 91.85 61.28 +12.44

My point here, though, is that both of them outperformed GRPO, which ought to be a

kind of advanced RL-based post-training method, a fine-tuning method.

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This is all you need to construct arbitrarily

complex workflows, data processing pipelines,

replication of business logic, etc.

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@lateinteraction e Creator of DSPy (and ColBERT!)

@maximerivest e Creator of Attachments

@tech_optimist e DSPy advocate, programmer, nice guy

@dbreunig e Writes excellent technical content

@DSPyOSS e Official DSPy account

@getpy e Curator of DSPyWeekly

@kmad e Me

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