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Slides: AI System Design: From Idea to Production - Apoorva Joshi, MongoDB

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AI System Design: From Idea to Production - Apoorva Joshi, MongoDB

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

Extracted Slides

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So, how will we "Vibe Code" in prod?

NOT that the product exists! Forget the code exists, but ErikSchulntz,Anthropic yibe coding in prod talk

World's Fair

d'sFai IN THE NEAR FUTURE.

OS..thepersonwhocommunicatesbestbecomes the "If you can communicate, you can program. programmer.

Sean Grove, OpenAI

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Specs are the new code

Evaluation criteria

System design

Product requirements

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Requirements Product System Design and Monitoring Evaluation Production Readiness 1

problem Identify the business and retrieval techniques Identify data sources guardrails Define system Optimize for accuracy

Identify your constraints stack architecture and.tech Select system Define offline evaluation metrics Optimize for. cost and latency

Define the role of AI feedback loops Determine the UX and Define online evaluation metrics Optimize for reliability

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Health insurance claims review

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Background

Background

Health insurers and heaith funds around the world employ medical reviewers to assess

policy. This requires cross-referencing clinical documentation, coverage policies, clinical roles in healthcare operations. guidelines, and patient claims history. It is one of the most administratively intensive

What we are building

We are building an internal claims review system for a fictitious health insurance submit claims and receive feedback on outcomes is out of scope. adjudicate claims. The external-facing system through which healthcare providers company called MDB Health. This system is used by medical reviewers to assess and

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Retrieval Augmented Generation (RAG)

Knowledge base

Context

Prompt

LLM

Answer

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AI Agents

Tools

Memory

Input

LLM

Action

Result

END

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Controlled flows?

LLM Call 1

LLM Call 2

Action 1

Action 2

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Tech stack decisions

Data processing

Hosting and inference

Foundation models

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Tech stack decisions contd...

Fine-tuning

Orchestration Frameworks

Embeddings and retrieval

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Define system guardrails

Guardrails define the boundaries of acceptable inputs and outputs.

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Definesystem guardrails

Guardrails define the boundaries of accepfable inpufs and outputs.

Inpu? Output

Invalid, irrelevanf or harmful inputs. Invalid, incorrect or harmfui outputs

Write mea poem. Findl resultshouldprovide citations

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Optimizingforaccuracy

Technique What it involves

Prompt engineering Write clear and well-structured prompts

Agent skills Progressive disclosure of information to agents

Query optimization Rewrite ordecompose queries forbetterretrievalorgenera

Reranking Reorder retrieved documents by relevance

Compaction Summarize or remove content from the LLM's context wind

Memory management Persist.information across sessions

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Key takeaways

Think deeply about your product requirements before having AI generate code.

Treat business and performance constraints as inputs to design, not afterthoughts.

Hidden Non-Slide Evidence

Classification audit: raw/sources/slide-ai-classification/slides/T0HhO4YtTfE/audit.json

Slide-Derived Subjects To Review

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