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
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Extracted Slides
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company that provides clinical
reasoning tools and solutions to
accelerate and automate healthcare
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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
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
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Failure mode datasets enable targeted product
iteration
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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
OCR text:
AIE
Putting it together
Microsoft
smol ai
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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.
OCR text:
AIE
Thankyou
pherovejyMD
chris@anterior.com
chrislovejoy.me
Anterior.com/Company
Microsoft
smol?
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