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Learn summit 2023 remote

Skill hiteshbandhu/skills-i-use/skills/ai-engineer-talks/learn-summit-2023-remote

Drop-in skills and plugins for your AI development workflows

Install

npx -y skills add hiteshbandhu/skills-i-use --skill learn-summit-2023-remote

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Applies AI Engineer Summit 2023 remote-talk playbooks for evals, hybrid RAG grounding, model selection, fine-tuning, prompt ops, multimodal apps, coding-agent maturity, and career transition from fullstack. Use when reproducing summit remote lessons, comparing LLM eval tools, or scoping hybrid retrieval; or when the user says "summit 2023 remote", "llmeval", "hybrid context query", "AI maturity model".

SKILL.md

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Learn Summit 2023 remote talks

Action playbook from eleven remote sessions at AI Engineer Summit 2023. Do not summarize talks — pick a workflow and execute it.

Supporting files:

workflows.md — workflows A–K
source-index.md — src-NNN → learnings

Optional deliverables: {SKILL_OUTPUT_DIR}/learn-summit-2023-remote/

Step 0 — Pick workflow

What is the user trying to do?
├─ Ship LLM evals in production (LLMeval)                    → A  [src-001]
├─ Hybrid context query / RAG grounding                    → B  [src-002]
├─ Viral AI app / codegen product (10k apps)               → C  [src-003]
├─ Career path: fullstack → AI engineer                    → D  [src-004]
├─ Pick the right model for a use case                     → E  [src-005]
├─ No-code / low-friction fine-tuning                        → F  [src-006]
├─ Prompt engineering tactics + prompt management            → G  [src-007]
├─ Multimodal TS apps (ModelFusion)                          → H  [src-008]
├─ Code AI maturity model (SAE levels)                       → I  [src-009]
├─ AI software engineer stack (embeddings, retrieval)        → J  [src-010]
└─ Generative infinite game / creative AI loops              → K  [src-011]

Open the matching section in workflows.md.

Install

cp -r skills/learn-summit-2023-remote ~/.claude/skills/
cp -r skills/learn-summit-2023-remote ~/.cursor/skills/

Source: playlists/ai-engineer-summit-2023-remote-talks/.

Cross-cutting rules

Rule	Source
Store prompts/configs; eval before fine-tune	[src-001 @ 0:00:53]
RAG needs deliberate pipeline, not naive retrieval	[src-002 @ 0:01:26]
Model choice is use-case specific, not one-size	[src-005 @ 0:00:45]
Prompt engineering ≠ prompt hoarding — use tooling	[src-007 @ 0:00:18]
Maturity model: partial → supervised agent autonomy	[src-009 @ 0:04:04]

Output to user

Name workflow (A–K) and deliverable
Artifacts under ./skill-outputs/learn-summit-2023-remote/ when requested

Invocation examples

@learn-summit-2023-remote set up evals like the LLMeval talk
hybrid grounding query design for our copilot
which workshop path for fullstack → AI engineer?

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.