ai-foundation-models
Skill1 starsChanged 6 months agoMITView on GitHub
Practical knowledge for understanding and working with foundation models (FMs) in AI engineering. Covers the AI engineering stack, planning AI applications, transformer architecture, training data, model size and scaling laws, post-training (SFT, RLHF), and sampling strategies (temperature, top-k, top-p, structured outputs).
In this repository21 other files it gives its agents.
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- ai-context-engine-productionSkill
- ai-context-engineSkill
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- ai-evaluationSkill
- ai-finetuningSkill
- ai-inference-optimizationSkill
- ai-multi-agent-mcpSkill
- ai-production-architectureSkill
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- ai-rag-and-agentsSkill
- ai-rag-defenseSkill
- ddia-architectureSkill
- ddia-batch-stream-processingSkill
- ddia-data-ethicsSkill
- ddia-data-modelingSkill
- ddia-replication-shardingSkill
- ddia-transactions-consistencySkill
- job-description-analyzerSkill
- resume-content-writerSkill
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The file
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1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 21
Check guidelines.md to find which files to load for your task
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI Foundation Models
Knowledge from "AI Engineering" by Chip Huyen (Chapters 1-2). Focuses on understanding what foundation models are, how they're built, and how to make sound architectural decisions when integrating them.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- Apply guidance to your work
Contents
References
| Category | Purpose |
ai-engineering-overview | Rise of AI engineering, language model basics, FM use cases |
planning-applications | Use case evaluation, defensibility, AI stack layers, AI vs ML eng |
training-data | Multilingual models, domain-specific models, data quality |
model-architecture | Transformer architecture, model size, scaling laws |
post-training | Supervised finetuning (SFT), preference finetuning (RLHF), reward models |
sampling | Temperature, top-k, top-p, structured outputs, hallucination, inconsistency |
Workflows
| Task | Workflow |
| Decide if/how to use FMs for a use case | workflows/evaluate-use-case.md |
| Configure sampling (temperature, top-k/p, structured output) | workflows/tune-sampling.md |
Guidelines
See guidelines.md for task-based file selection.
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