# SylphAI-Inc/LLM-engineer-handbook

A curated list of Large Language Model resources, covering model training, serving, fine-tuning, and building LLM applications.

Repository: https://github.com/SylphAI-Inc/LLM-engineer-handbook
Canonical: https://ross.abutalabs.com/products/llm-engineer-handbook
License: MIT
License Family: permissive
Last push: 2025-08-18T22:45:29+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 37, release rhythm 35, longevity 47
- inputs: {"age_days": 667, "days_push": 380, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5023, forks 713 (observed 2026-08-28T04:09:04.891925+00:00)

## What it is
A curated list of Large Language Model resources covering the full LLM lifecycle: pretraining, fine-tuning, serving, prompt management, and building LLM applications. It aggregates libraries, frameworks, tutorials, books, and community resources to help engineers build production-grade LLM systems.

## Use cases
- find frameworks for fine-tuning large language models
- learn how to serve LLMs in production
- discover RAG and agent libraries
- find tutorials on prompt optimization
- learn the LLM engineering lifecycle end to end
- find LLMOps and evaluation tools
- locate datasets and benchmarks for LLM training

## When to choose
- you are an engineer navigating the LLM tooling ecosystem
- you want a curated starting point for learning LLM engineering
- you need to compare frameworks for training, serving, or building LLM apps

## When to avoid
- you need runnable software rather than a resource list
- you want deep original documentation on a single tool

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-training, llm-inference, rag, prompt-engineering, agent-framework
- domain: large-language-models, artificial-intelligence, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: curated-list, llm, fine-tuning, llmops, model-serving, handbook

## Member repositories
- SylphAI-Inc/LLM-engineer-handbook (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:04.891925+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:17:28.933664+00:00, confidence not recorded.
  - readme: https://github.com/SylphAI-Inc/LLM-engineer-handbook (fetched 2026-08-28T04:09:04.891925+00:00, sha 271d9b73eab5)
- Data as of 2026-08-30T08:39:29.467469+00:00.