References: Project walkthrough

Source material

Source curriculum (structural mirror, cited as further study):
• Full Stack Deep Learning, "LLM Bootcamp" (Spring 2023):
    Project Walkthrough (askFSDL)
  Instructors: Charles Frye, Sergey Karayev, and Josh Tobin
  Course page: https://fullstackdeeplearning.com/llm-bootcamp/
  Lecture videos: publicly available on the Full Stack Deep Learning
    YouTube channel
  License: bootcamp materials are published free to view but no explicit
    license (Creative Commons or otherwise) is published; lecture videos
    are on YouTube under standard terms.
  Required attribution: "Based on the structure of the Full Stack Deep
    Learning LLM Bootcamp (Spring 2023), by Charles Frye, Sergey Karayev,
    and Josh Tobin (fullstackdeeplearning.com/llm-bootcamp). This is an
    independent structural mirror in original prose; it reproduces no
    course materials, and Full Stack Deep Learning does not endorse it."
This lesson mirrors the STRUCTURE of the corresponding bootcamp session (the askFSDL walkthrough)
without reproducing the application's code. Clawdemy's lessons are original
prose that describes the production decisions a real app of that shape
embeds, framed against the components built in lessons 1-4.

Watch this next

• Full Stack Deep Learning, LLM Bootcamp: Project Walkthrough (askFSDL) by Charles Frye, Sergey Karayev, and Josh Tobin. The session this lesson mirrors. The recorded version walks the actual askFSDL code; pair it with this lesson to see the decisions named here in the bootcamp’s own implementation.

Going deeper

A short, durable list. Each link is a specific next step, not a generic pile.

• Browse open-source LLM apps on Hugging Face Spaces. The fastest way to read more real LLM applications: most Spaces are open source; you can click “Files” on any app and read the pipeline. Apply this lesson’s read-this-design checklist as you browse.

• The LangChain “use cases” gallery. End-to-end LangChain tutorials, each of which embeds the same kinds of decisions read here. Read for the decisions, not for the LangChain idioms; the decisions are portable.

• The Streamlit gallery. UI-side worked examples for the rendering layer. Many include LLM applications and show how the call-site decisions (streaming, citations) become UI patterns.

Adjacent topics

Where this connects inside the track.

• Launch an LLM app in one hour (lesson 1). The minimum app whose five-component shape askFSDL extends.

• Prompt engineering, “Learn to Spell” (lesson 3). The scope-honest, citation-asking system prompt this walkthrough names is exactly the prompt-engineering discipline from lesson 3.

• Augmented language models (lesson 4). The RAG pipeline’s seven moving parts are the pipeline askFSDL embeds; this lesson reads the production decisions baked into them.

• UX for language user interfaces (lesson 6). What askFSDL deferred at the UX layer.

• LLMOps (lesson 7). What askFSDL’s “log enough to debug” seeds, the full operational layer that wraps it.