Cohort 1

Systems Analyst

Kevin Zhong

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Using AI coding assistants and tools to write, review, and ship code faster.

Build the core systems yourself. Diagnose failures, make tradeoffs, and develop engineering judgment that outlasts today’s frameworks.

Author of the Common-Sense Guide Series

Jay Wengrow

Author of A Common-Sense Guide to AI Engineering, Software Engineer and Educator

Common-Sense AI Engineering: Build LLM Apps & Agents from the Ground Up

Actualize

<h3>Why I enrolled</h3>
<p class="p2-tall">I'd like to add being able to build LLM apps and services as part of my toolkit; I also want to start developing an intuitive understanding at the granular level of how the "black box" works.</p>
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    <h3>A breakthrough moment</h3><p class="p2-tall">How RAG actually works - previously, I was pretty unsure about the extent of separation between the LLM itself, and the model responsible for embedding/vectorizing and retrieving the knowledge source.<br><br>Learning that the embedding model is a <em>separate</em> model helps enormously; the actual pipeline itself feels like it's becoming increasingly clear:<br><br>Prompt -&gt; Search for Most Similar Chunks to Prompt in Vector Database -&gt; Retrieve Chunks and Add to Prompt -&gt; Send Retrieval Enhanced Prompt to LLM.</p>

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<h3>What I’ll apply</h3>
<p class="p2-tall">Very excited to dive further into agents, and potentially present custom LLM bots as demos to open up alternatives to my org's current dependence on vendored low-code solutions. I'm particularly going to look into integrating deterministic document parsers into an agentic workflow to provide non-technical stakeholders with an easy way of validating client-provided sample responses with out endpoints.</p>
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Certificate

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