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

Source Video

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

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

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DSPy

Overview

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

https://dspy.ai

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Taking:NYC(NYT)46.17Centra

Use Cases Lighting Round

What we'll cover:

Simple sentiment classifier

Structured information from a PDF

Multimodal extraction

Web research agent (using Tools)

Detectboundaries of a document

github.com/kmad/aie

Recursivelysummarize 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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(unless you

S

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P

aowsyoutoretai

Taking:NYC(NYT) 46.17 Central

detailed control over your program while focusing on

things that actually matter

Allows you to create computer programs thatuse'LLMs

as inlinefunction calls

Why I'm such

Programswhichyouhappentobe abletooptimize-it's a

programmingparadigm,notawholesaleframework,andnot

an advocate

'optimizer-first"

Is built with a systems mindset;you encode intent and

structurein awaythatis transferable

Yourprogram design likely moves slower thanAI advancements(at

least sofar)

slide-007.jpg

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this way of working

found it useful - the hope is to

tives foryou to extrapolate to

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Core Concepts

Signatures

Modules

Tools

Specifywhatyouwant,

Structure your program

Interactwiththeoutside

nothow;lettheLLM

logically

world-or therestofthe

figure it out

program

Adapters

Optimizers

Metrics

Customizableprompt

OptimizeyourDSPy

Definewhattooptimize

formatters:thinkJSON,

program,ML-style

against (can be multiple

BAML,XML,etc.

things)

(lettheLLM figure it out!)

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How you“express your

Signatures

declarativeintent”

Can be simple strings or

complex Class-based objects

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input text to classify sentiment

he more positive

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D

pdf_link

sdaau

ront.net/CIK-0001045810/8b76daec-a85f-429a-968c

Taikng:NYC(NYT)46.17Central

Multimodality

doc=Attachments(pdf_link)

rag = dspy.ChainofThought("question,document->answer")

FORM4

result =rag(question="How many shares were sold in total?",document=doc)

print(result)

Prediction(

reasoning='The document isa Form 4filing reporting changes in beneficial

ownership of securities by Mark A.Stevens.It lists two transactions involving the

sale of common stock shares on two dates:\n\n-On 9/11/2025,200,000 shares were

sold.\n-On 9/12/2025,297,797 shares were sold.\n\nTo find the total number of

shares so1d,we sum these tw0 amounts:\n\n200,000 +297,797=497,797 shares so1d

in total.\n\nNo other sales transactions are listed in the document.

answer='497,797sharesweresoldin total.'

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ceporting changes in beneficial

lists two transactions involving the

Qn9/11/2025,200,000 shaxes were

naTofind the total number of

297,797=497,797 shares8o1d

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G

Taking:NYCNYT)46.17Cental

Optimizers

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

program. This allows you to quantitatively improve your performance and

costprofile.

"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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Taikng:NYCNYT)46.17Cent

DSPy is not an optimizer. It's set of

programming abstractions (signatures, modules)

that can be optimized.

Omar Khattab @lateinteraction

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D

Taikng:NYC(NYT)46.17Centra

Thereason thatthisistrickyisquitesubtle.It's thefact that

anytimeyou use an LLM to assign areward,thoseLLMs aregiant

thingswith billionsofparameters,and they'regameable.Ifyou're

reinforcementlearningwithrespect to them,you willfind

-AndrejKarpathy

adversarial examplesforyourLMjudgesalmostguaranteed.

(via theDwarkesh

Podcast)

Soyou can't do thisfor too long.You domaybe10 steps or20

steps,andmaybeitwillwork,butyoucantdo100or100.I

understand it'snotobvious,butbasically themodelwill find little

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

crannies of the giantmodel and find a way to cheat it

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GEPA:REFLECTIVEPROMPTEVOLUTIONCANOUTPERFORM

Taikng:NYC(NYT)46.17Centra

REINFORCEMENTLEARNING

LakshyaAAgrawal,ShangyinTan，DilaraSoylu²NoahZiems,

RishiKhare',Krista Opsahl-Ong,ArnavSinghvi2s,HerumbShandilya²,

MichaelJRyan,MengJiang',ChristopherPotts²,KoushikSen'

AlexandrosG.Dimakis,IonStoica'DanKlein',MateiZahariaOmarKhattab

Chris Potts

https://www.youtube.com/

watch?v=0bkwd9OYqfk

Model

HotpotQA

IFBench

Hover

PUPA

Aggregate

Improvement

Qwen3-8B

Baseline

MIPROv2

GRPO

GEPA

My point here,though,is that both of them outperformed GRPO,which oughttobea

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

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Taikng:NYC(NYT) 46.17Centra

This is all you need to construct arbitrarily

complex workflows, data processing pipelines,

replication of business logic, etc.

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SNOW!

Ssopshere

Tang:NYC(NYT) 46.17Centa

DSPy on X

@lateinteraction

CreatorofDSPy(and ColBERT!)

@maximerivest

CreatorofAttachments

@tech_optimist

DSPy advocate, programmer, nice guy

@dbreunig

Writesexcellenttechnicalcontent

@DSPyOSS

OfficialDSPy account

@getpy

Curator of DSPyWeekly

@kmad

Me

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SatNov22440PM

PPo

dspy_workshop.ioymb U

S

DSPY_WORKSHOP

Tang NYC(NYT） 46.17Centl

mtntSetup>O Setupobservabiity

venv

vscode

+Code

+Markdown

InterruptRestartCler Al OutputsGoTo|upyter Variables

Outine

venv (Python3.11.13)

data

Set up observability

DD日自

module_example

fromphoenix.otelimportregister

modules

configure thePhoenixtracer

tracer_provider=register(

BoundaryDetector.py

DocumentClassifier.py

auto_instrumentaTrueAut&-instrument your app based on installed OI dependencles

DocumentProcessor.py

Summarizer.py

Python

dezeen

usecs/karaDmlods/de/dssy_rkshopxyLb/otho3.11/site-Rackages/tomutey:21:Tqdrning:IProgressnot found.Please pdate jupyterand ipywidgets.Seehtts//iay

VisuaWithTools.py

from.autonotebookLnporttqdn asnotebook_tqdm

output

main.py

1,M

U,o

3o.example

gitignore

python-version

U

dspy.workshop_cle.

dspworkshop.ipynb

U

hub.ipynb

LICENSE

pyproject.toml

Problem

ndino

Debug Console

TerminalPorts

Gitlens

Jupyter

READMEmd

adura@MADCAPM28857:~/D/d/d/modute_exampte]-[16:39:55]-[V:.verv]-[G:main=]

time_entry_optimizer.

uv.lock

OUTLINE

Environment Setup

MUseful tidbits

Mi Smple Sentiment..

MStructured informatio

Dynamically genera..

MAdapter Example

XK10

slide-021.jpg

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A

·SatNov 22440PM

PPO

dspy_workshop.ipymbU

aeS0

FO

DSPY_WORKSHOP

dspywor（functLon）def toad_dote(

Tang:NYC (NYT) 4617 Centl

env

+Code

dotenv_path:StrPath|None=None,

stream:Io[str]|None=None,

env (Python3.11.13)

vicode

data

Lopt/hs

verbose: boolFalse,

skipped unsupported reflection of expression-based inde:

oexti

override:boolFalse,

module_example

LoRt/hs

interpolate: boolTrue,

kipped unsupported reflection of expression-based 1nde

modules

pexti

OTo）b001

encoding:str]None=“utf-8"

BoundaryDetector.py

for

DocumentClassifier.py

DocumentProcessor.py

Parameters:

doten_path:Absolute or relative path to eovfle.

Summarizer.py

stream:Text stream（tuchaso.StringIO）wiheev contnt,ed

AdozAens

doteynothN

VisuWithTools.py

load_dotenv(overrideTrue)

output

0.0s

Python

main.py

1M

True

OAN

U,o

5example

gitignore

inportos

python-version

U

LM_CONFIG·（

M

"nodel":os.getenv("DSPY_FAST_MODEL"),

dsp_workshop.pynb

"ap_key":os.getenv（-OSPY_API_KEY"),

u

"api_base”:os.getenv("DSPY_ENDPOINT"),

hubioynb

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"ap1_version:os.getenv("DSPY_FAST_API_vERSION"),

KLICENSE

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pyproject.toml

Problem

Terminal

Ports

Gitens

Jupyter

README.md

r[ks

adgraMADCAPM28857:~/D/d/d/modute_exanple]-[16:39:55]-[V:.ve]-[G:nain=]

M

time_entry.optimizer.

uvlock

OUTLINE

Environment Setup

MUseful tidbits

M Simple Sentiment...

MStructuredinformatio

Dynamically genera..

MAdapter Example

6A8

slide-022.jpg

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Q·SatNov22444PM

dspy_workshop.ipynbU

dspy_workshop_clean.ipynbM

hub.ipyn

Summarizer.py

Taing:NYC (NYT) 46.7Centa

dspy_workshop_clean.ipynb>M Structured information from a PDF> #Attachments/multimodal / structured output

+Code

+Markdown

Interrupt

RestartClearAllOutputsQGoToJupyterVariablesOutline

verv（Python 3.11.13)

口

from attachments.dspy import Attachments

D日

doc=Attachnents（pdf_link)

0.9s

Python

[Attachments]

Running prinaryprocessor'pdf_to_llm'forhttps://d18rn@p25nr6d.cloudfront.net/C1K-00e1e4581e/8b76dacc-a85f-429a-968c-3c3c9dbac3fc.pdf

[Attachments]

Applyingstep'load.url_to_response'tohttps://d18rn0p25owr6d.cloudfrontnet/CIK-0001045810/8b76daec-a85f-429a-968c-3c3c9dbae3fc.pdt

[Attachments]

Applying step‘modify.norph_to_detected_type'to httas://d18rn@g25nmr6d.cloudfront.net/CIK-0001045810/8b76daec-a8Sf-429a-968c-3c3c9dbae3fc.pdf

[Attachments]

Applyingstep'load.pdf_to_pdfplumber'tohttps://d18rneo25mmr6d.cloudfront.net/CIK-0801045810/8b76daec-a85f-429a-968c-3c3c9dbae3fc.pdf

[Attachments]

Applying stepmodity.pages'tohttos://d18rnep25nwr6d.cloudfcont.net/cIK-e001845810/8b76daec-a85f-429a-968c-3c3c9dbae3fc.pdf

[Attachments]

Running AdditivePipeline(present.markdown+present.images+present.metadata)

[Attachments]

Applyingadditivestep'present.markdown'tohttps://d18rneo25mr6d.cloudfront.net/CIK-0001045810/8b76daec-a851-429a-968c-3c3c9dbae3fc.pd1

[Attachments]

Applying additive step'present.inages'tohttps://d18rnlo25mar6d.cloudfront.net/CIK-ee0104581e/8b76dacc-a85f-429a-968c-3c3c9dbae3fc.pdf

Thisiswhat we're working with

from IPython.display inport Image,display

display(Image(url=doc.inages[e]))

Python

from https://maximerivest.github.1o/attachments/architecture/sdspy-integration

Problems

Output

Debug Console

Terminal

Ports

GitLens

Jupyter

fish-mod_oxample+①

XKtOg

Pmain

Launchpad

86A8

CursorTab

Spaces:4

Cell19of59

AmpTab

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A

2·SatNov 22447PM

口

口口

dspy_workshop.ipynbU

dspy_workshop_clean.ipynbM

hub.ipyn

summarization.ipynb M

Summarizer.py

Takng:NYC (NYT) 46.17Centra

dspy_workshop_clean.ipynb>M Structured information from a PDF>MDynamicall generate a schema based on the content itself>from pydantic import BaseModel, Field

+Code

十Markdown

RunAllRestartClearAllOutputs|JupyterVariablesOutline

venv(Python 3.11.13)

""-A transaction in the document.

D

transaction_type:str=Field(description="The type oftransaction.")

D日

shares_sold:int =Field(descrintinn="Thenimher nf shares snln.)

price_per_s(class）str

total_price

str(object=")->str str(bytes_or_buffer[encodingL errors]l)->str

class Docunents Create anew stringobjectfrom the given object.f encodingorerrorsis specified,then the objectmustexpose a data buffer that wil e

Astructdecodedusingtgienencodinganderrorhanderthewisereturnsthresultfbjctst_Ofdfined）orreprobjct）encoding

formtype:defaults tosys.getefuencoing.ersdfalts tostic

filing_date: str=Field（descriptionaThe date of the filing in the format YyyY-MM-Do.)

total_shares,sold:int

transactions:list[Transaction]=Field(description"Alist of transactions.")

class DocumentAnalyzerSchena(dspy.Signature):

Analyze document content and extract insights.

MIEN

document:Attachnents=dspy.InputField()

document_schena:DocunentSchena=dspy.OutputField(desc="A structured representation of the document content.")

result_schema=dspy.ChainofThought(DocumentAnalyzerSchena)(docunent=doc)

pprint(dict{Any,Any)(result_schena.document_schema))

#Now use the typed object to get the total shares sold

print(\n\n#Total shares sold:)

print(result_schena.document_schena.total_shares_sold)

0.0s

Python

{'filing_date':'2025-09-15',

'form_type':Form 4',

I ±

Problems

Output

Debug Console

Terminal

Ports

GitLens

Jupyter

fish-modue_example+①自

X

XKtooe

main

Launchpad

?6A8

CursorTab

Spaces:4

Cell26of59

AmpTab

Dense Scene-Detected Slide Candidates