AI engineering — source index
Master bibliography for the ai-engineering/ knowledge base. Every URL referenced across all topic articles, organized by category.
Count: 54 sources. Last curated: 2026-03-22.
Standards: Primary research (arXiv, peer-reviewed venues), official vendor docs, established practitioners (Weng, Huyen, Karpathy, Willison, Alammar), recognized standards bodies (NIST, OWASP). Blog posts included only when they offer unique insight; marked as such.
Research papers
| # |
Authors / Title |
Venue |
Year |
Topic |
URL |
| 1 |
Vaswani et al., Attention Is All You Need |
NeurIPS |
2017 |
LLM foundations |
arXiv:1706.03762 |
| 2 |
Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks |
NeurIPS |
2020 |
Context engineering |
arXiv:2005.11401 |
| 3 |
Kaplan et al., Scaling Laws for Neural Language Models |
— |
2020 |
LLM foundations |
arXiv:2001.08361 |
| 4 |
Hu et al., LoRA: Low-Rank Adaptation of Large Language Models |
ICLR |
2022 |
Full-stack LLM |
arXiv:2106.09685 |
| 5 |
Hoffmann et al., Training Compute-Optimal Large Language Models (Chinchilla) |
NeurIPS |
2022 |
LLM foundations |
arXiv:2203.15556 |
| 6 |
Ouyang et al., Training Language Models to Follow Instructions with Human Feedback |
NeurIPS |
2022 |
Full-stack LLM |
arXiv:2203.02155 |
| 7 |
Yao et al., ReAct: Synergizing Reasoning and Acting in Language Models |
ICLR |
2023 |
Agentic AI |
arXiv:2210.03629 |
| 8 |
Schick et al., Toolformer: Language Models Can Teach Themselves to Use Tools |
NeurIPS |
2023 |
Agentic AI |
arXiv:2302.04761 |
| 9 |
Liu et al., Lost in the Middle: How Language Models Use Long Contexts |
TACL |
2023 |
Context engineering |
arXiv:2307.03172 |
| 10 |
OpenAI, GPT-4 Technical Report |
— |
2023 |
LLM foundations |
arXiv:2303.08774 |
| 11 |
Zhao et al., A Survey of Large Language Models |
— |
2023 (updated 2025) |
LLM foundations |
arXiv:2303.18223 |
| 12 |
Rafailov et al., Direct Preference Optimization |
NeurIPS |
2023 |
Full-stack LLM |
arXiv:2305.18290 |
| 13 |
Dettmers et al., QLoRA: Efficient Finetuning of Quantized Language Models |
NeurIPS |
2023 |
Full-stack LLM |
arXiv:2305.14314 |
| 14 |
A Survey on the Memory Mechanism of LLM-based Agents |
— |
2024 |
Agentic AI |
arXiv:2404.13501 |
| 15 |
Maximum Effective Context Window |
OAJAIML |
2024 |
Context engineering |
oajaiml.com |
| 16 |
Meta, The Llama 3 Herd of Models |
— |
2024 |
LLM foundations |
arXiv:2407.21783 |
| 17 |
Beyond ReAct: Planner-Centric Framework for Complex Tool-Augmented LLM Reasoning |
— |
2025 |
Agentic AI |
arXiv:2511.10037 |
| 18 |
PROBE: Measuring Proactive Problem Solving in LLM Agents |
— |
2025 |
Agentic AI |
openreview.net |
| 19 |
(Mis)Fitting: A Survey of Scaling Laws |
— |
2025 |
LLM foundations |
arXiv:2502.18969 |
| 20 |
Post-Training Scaling survey |
ACL |
2025 |
Full-stack LLM |
aclanthology.org |
| 21 |
DeepSeek, DeepSeek-R1 Technical Report |
— |
2025 |
LLM foundations |
arXiv:2501.12948 |
| 22 |
The Landscape of Agentic RL for LLMs: A Survey |
TMLR |
2026 |
Agentic AI |
arXiv:2509.02547 |
| 23 |
Context Engineering vs Prompt Engineering (9,649 experiments) |
— |
2026 |
Context engineering |
keepmyprompts.com |
Official vendor documentation
Established practitioners (blogs, talks, courses)
Standards and governance
Source distribution
| Category |
Count |
| Research papers |
23 |
| Vendor documentation |
13 |
| Frameworks and tools |
4 |
| Practitioner content |
11 |
| Standards and governance |
3 |
| Total |
54 |
Update this file whenever you add sources to topic articles. Note date and delta.