---
title: "Slides: Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior"
category: "slides"
video_id: "MRM7oA3JsFs"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior

## Source Video
[Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior](https://www.youtube.com/watch?v=MRM7oA3JsFs)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/MRM7oA3JsFs/slide-001.jpg]]

OCR text:

> INNOVATIONPARTNER
> aws
> PLATINUMSPONSORS
> Graphite
> WWindsurf
> MongoDB
> daily
> augment code
> Workos

![[assets/slides/MRM7oA3JsFs/slide-002.jpg]]

OCR text:

> Own yourvertical Aolaysook for buildinga  o
> comain-native LLM application ha,
> i”
> i oO
> | a Microsoft Qo)

![[assets/slides/MRM7oA3JsFs/slide-003.jpg]]

OCR text:

> Anterior
> We're a New York-based, clinician-led
> company that provides clinical
> reasoning tools and solutions to
> accelerate and automate healthcare
> administration

![[assets/slides/MRM7oA3JsFs/slide-004.jpg]]

OCR text:

> OUP BET FOR VERTICAL AT APPLICATIONS:
> 
> an
> 
> LE ee ese
> 
> 5 ra
> SYSTEM Tor an pane Lae Megas yaseaeaacdllet
> ; le : SOONISTICATION
> I RICS Se Olee kel ale we ay veliaane aia
> ate tetclealaicie igi een eee
> ec and pipelines
> c

![[assets/slides/MRM7oA3JsFs/slide-005.jpg]]

OCR text:

> Wirvy is tt arc! to successfully agply
> | Lis to specialized incustries’
> mvt i)
> | mw Microsoft §GDOU

![[assets/slides/MRM7oA3JsFs/slide-006.jpg]]

OCR text:

> An exampic cinical case processed by
> On eee ne oa ee eet
> Gh ls there documentation of unsuccesstul
> merinpakvic tarrearis fae atk aan ae
> conservative therapy for at least 6 weeks?
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![[assets/slides/MRM7oA3JsFs/slide-007.jpg]]

OCR text:

> We achieved a paseline performance of 95% tor approvind Care...
> | V1 fai)
> mw Microsoft § SOU

![[assets/slides/MRM7oA3JsFs/slide-008.jpg]]

OCR text:

> KLAS CASE STUDY
> We achieved a baseline performance of 95% for approving care...
> then iterated with a specific customer to >99% within 8 weeks
> Initial Results 8 Weeks Later
> Performance (F1-Score)
> 95.73% Performance (F1-Score)
> 99.24%
> using the system
> outlined in this talk

![[assets/slides/MRM7oA3JsFs/slide-009.jpg]]

OCR text:

> (2) Empower domain experts to define and
> maintain a failure mode ontology
> Photo extraction
> Table extraction
> Handwriting extraction
> Medical record extraction
> Checkbox extraction
> Medical Necessity Review
> Failure Modes
> Logic representation
> Rules interpretation
> Rule source selection
> Clinical reasoning
> Under-inference
> Over-inference
> Chronological reasoning

![[assets/slides/MRM7oA3JsFs/slide-010.jpg]]

OCR text:

> (3) Measure eras together
> on oroduction data to uniock tarceted Insiqnts
> ; i. a . 7 ; oe a os
> Ree eee ae
> i | 7 |
> | ' wick aS 000 Oh

![[assets/slides/MRM7oA3JsFs/slide-011.jpg]]

OCR text:

> AIE
> MEASURE
> SpecfyFailuremode'tofx
> Makechanges
> ReviewAIoutputs
> Analyze perfornance
> toprioritisework
> Produetion
> Performance
> opplication
> insights
> "Failure mode
> dataset"eval
> Chnica!
> AI
> ChnicalPA
> engineer(s)engineer(s)
> Make decisions
> aboutnaking ive
> See inpact of changes
> aws

![[assets/slides/MRM7oA3JsFs/slide-012.jpg]]

OCR text:

> Failure mode datasets enable targeted product
> iteration

![[assets/slides/MRM7oA3JsFs/slide-013.jpg]]

OCR text:

> AIE
> Empower your domain experts to make
> improvements directly with tooling and evals
> Application Pipelines
> Tooling for
> domain experts
> Domain evals
> Domain
> Knowledge Base
> Microsoft
> smol.ai

![[assets/slides/MRM7oA3JsFs/slide-014.jpg]]

OCR text:

> AIE
> Putting it together
> Microsoft
> smol ai

![[assets/slides/MRM7oA3JsFs/slide-015.jpg]]

OCR text:

> Takeaways
> • To build domain-native LLM applications you need to solve the last mile problem.
> • Using the best models isn't enough - you should build an adaptive domain intelligence engine.
> • Domain experts power this system by reviewing AI outputs to generate performance metrics, failure modes and suggested improvements.
> • This takes production data and uses it to give your LLM product a nuanced understanding of customer workflows.
> • The result is a self-improving, data-driven process that can be managed by a domain expert PM.

![[assets/slides/MRM7oA3JsFs/slide-016.jpg]]

OCR text:

> AIE
> Thankyou
> pherovejyMD
> chris@anterior.com
> chrislovejoy.me
> Anterior.com/Company
> Microsoft
> smol?

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.