AI Engineering Mastery bibliography

Standards-body + government · 8

1. MITRE Corporation (2024). ATLAS: Adversarial Threat Landscape for Artificial-Intelligence Systems. MITRE. https://atlas.mitre.org/
2. MITRE Corporation (2024). ATLAS Tactics: ML Supply Chain Compromise and AI Model Inference. MITRE. https://atlas.mitre.org/tactics/
3. MITRE Corporation (2024). ATLAS Adversarial Threat Landscape for AI Systems. MITRE. https://atlas.mitre.org/
4. MITRE Corporation (2024). ATLAS Matrices. MITRE. https://atlas.mitre.org/matrices/
5. OWASP Foundation (2024). OWASP Top 10 for Large Language Model Applications. OWASP. https://owasp.org/www-project-top-10-for-large-language-model-applications/Cited in 2 modules
6. OWASP Foundation (2024). LLM01: Prompt Injection. OWASP. https://owasp.org/www-project-top-10-for-large-language-model-applications/
7. OWASP Foundation (2024). LLM06: Sensitive Information Disclosure. OWASP. https://owasp.org/www-project-top-10-for-large-language-model-applications/
8. OWASP Foundation (2024). LLM08: Excessive Agency. OWASP. https://owasp.org/www-project-top-10-for-large-language-model-applications/

Other primary sources · 71

1. Meta AI (2024). Llama Model Card. Meta Platforms. https://llama.meta.com/
2. Cloud Security Alliance (2024). AI Controls Matrix. Cloud Security Alliance. https://cloudsecurityalliance.org/research/working-groups/ai-controls
3. Anthropic (2024). Building with Claude: Engineering Documentation. Anthropic. https://docs.anthropic.com/
4. Anthropic (2024). Models Overview. Anthropic. https://docs.anthropic.com/en/docs/about-claude/models
5. Anthropic (2024). Prompt Engineering Overview. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
6. Anthropic (2024). Tool Use with Claude. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/tool-use
7. Anthropic (2024). Prompt Caching. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
8. Anthropic (2024). Be Clear, Direct, and Detailed. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/be-clear-and-direct
9. Anthropic (2024). Introducing Contextual Retrieval. Anthropic. https://www.anthropic.com/news/contextual-retrieval
10. Anthropic (2024). Citations. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/citations
11. Anthropic (2024). Evaluation Tool. Anthropic. https://docs.anthropic.com/en/docs/test-and-evaluate/eval-tool
12. Anthropic (2024). Building Effective Agents. Anthropic. https://www.anthropic.com/research/building-effective-agentsCited in 2 modules
13. Anthropic (2024). When to Fine-Tune Claude. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/fine-tuning
14. Anthropic (2024). Vision. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/vision
15. Anthropic (2024). Streaming Messages. Anthropic. https://docs.anthropic.com/en/api/messages-streaming
16. Anthropic (2024). Pricing. Anthropic. https://www.anthropic.com/pricing
17. Anthropic (2024). Message Batches API. Anthropic. https://docs.anthropic.com/en/docs/build-with-claude/batch-processing
18. Anthropic (2024). Mitigating Jailbreaks and Prompt Injections. Anthropic. https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/mitigate-jailbreaks
19. Anthropic (2024). Claude Model Card. Anthropic. https://www-cdn.anthropic.com/
20. Apple (2024). Apple Intelligence Foundation Language Models. Apple. https://machinelearning.apple.com/research/apple-intelligence-foundation-language-models
21. Bagdasaryan, E., Hsieh, T.-Y., Nassi, B., and Shmatikov, V. (2023). Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs. arXiv preprint. https://arxiv.org/abs/2307.10490
22. Chen, L., Zaharia, M., and Zou, J. (2023). FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance. arXiv preprint. https://arxiv.org/abs/2305.05176
23. Google Cloud (2024). Document AI. Google. https://cloud.google.com/document-ai/docs
24. Cloudflare (2024). AI Gateway Documentation. Cloudflare. https://developers.cloudflare.com/ai-gateway/
25. Cohere (2024). Rerank Documentation. Cohere. https://docs.cohere.com/docs/overview
26. Cormack, G. V., Clarke, C. L. A., and Buettcher, S. (2009). Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods. ACM SIGIR Conference. https://doi.org/10.1145/1571941.1572114
27. DecipherU (2026). AI Engineering Mastery Capstone Rubric. DecipherU. https://decipheru.com/ai/courses/ai-engineering-mastery
28. Google DeepMind (2024). Gemini API: Models. Google. https://ai.google.dev/gemini-api/docs/models/gemini
29. Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. arXiv preprint. https://arxiv.org/abs/2305.14314
30. Gao, L., Ma, X., Lin, J., and Callan, J. (2022). Precise Zero-Shot Dense Retrieval without Relevance Labels (HyDE). arXiv preprint. https://arxiv.org/abs/2212.10496
31. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., and Wang, H. (2024). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint. https://arxiv.org/abs/2312.10997
32. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., and Fritz, M. (2023). Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv preprint. https://arxiv.org/abs/2302.12173
33. Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv preprint. https://arxiv.org/abs/1503.02531
34. Hodgson, P. (2017). Feature Toggles (aka Feature Flags). martinfowler.com. https://martinfowler.com/articles/feature-toggles.html
35. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv preprint. https://arxiv.org/abs/2106.09685
36. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv preprint. https://arxiv.org/abs/2005.11401
37. Liang, P., Bommasani, R., Lee, T., et al. (2022). Holistic Evaluation of Language Models (HELM). arXiv preprint. https://arxiv.org/abs/2211.09110
38. Microsoft (2024). PyRIT: Python Risk Identification Tool for generative AI. Microsoft. https://github.com/Azure/PyRIT
39. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2019). Model Cards for Model Reporting. arXiv preprint. https://arxiv.org/abs/1810.03993
40. Muennighoff, N., Tazi, N., Magne, L., and Reimers, N. (2023). MTEB: Massive Text Embedding Benchmark. arXiv preprint. https://arxiv.org/abs/2210.07316
41. Nygard, M. (2018). Release It! Design and Deploy Production-Ready Software (2nd ed.). Pragmatic Bookshelf. https://pragprog.com/titles/mnee2/release-it-second-edition/
42. OpenAI (2024). OpenAI Platform Documentation. OpenAI. https://platform.openai.com/docs
43. OpenAI (2024). Prompt Engineering Guide. OpenAI. https://platform.openai.com/docs/guides/prompt-engineering
44. OpenAI (2024). Structured Outputs. OpenAI. https://platform.openai.com/docs/guides/structured-outputs
45. OpenAI (2024). Prompt Caching. OpenAI. https://platform.openai.com/docs/guides/prompt-caching
46. OpenAI (2024). Embeddings Guide. OpenAI. https://platform.openai.com/docs/guides/embeddings
47. OpenAI (2024). Fine-Tuning Guide. OpenAI. https://platform.openai.com/docs/guides/fine-tuning
48. OpenAI (2024). Vision. OpenAI. https://platform.openai.com/docs/guides/vision
49. OpenAI (2024). Realtime API. OpenAI. https://platform.openai.com/docs/guides/realtime
50. OpenAI (2024). Pricing. OpenAI. https://openai.com/api/pricing/
51. OpenAI (2024). Batch API. OpenAI. https://platform.openai.com/docs/guides/batch
52. OpenAI (2024). Moderation Guide. OpenAI. https://platform.openai.com/docs/guides/moderation
53. OpenTelemetry (2024). Semantic Conventions for GenAI. Cloud Native Computing Foundation. https://opentelemetry.io/docs/specs/semconv/gen-ai/
54. Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training Language Models to Follow Instructions with Human Feedback. arXiv preprint. https://arxiv.org/abs/2203.02155
55. Pinecone (2024). Pinecone Documentation. Pinecone Systems. https://docs.pinecone.io/
56. Qdrant (2024). Qdrant Documentation. Qdrant. https://qdrant.tech/documentation/
57. Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust Speech Recognition via Large-Scale Weak Supervision (Whisper). arXiv preprint. https://arxiv.org/abs/2212.04356
58. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv preprint. https://arxiv.org/abs/2305.18290
59. Reimers, N. and Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint. https://arxiv.org/abs/1908.10084
60. National Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
61. National Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
62. National Institute of Standards and Technology (2024). Artificial Intelligence Risk Management Framework: Generative AI Profile (NIST AI 600-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1
63. National Institute of Standards and Technology (2024). Generative AI Profile (NIST AI 600-1): Measure Function. U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1
64. National Institute of Standards and Technology (2024). AI RMF Generative AI Profile (NIST AI 600-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1
65. National Institute of Standards and Technology (2024). AI RMF Generative AI Profile: Measure Function. U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1
66. Vercel (2024). Vercel AI Gateway. Vercel. https://vercel.com/docs/ai-gateway
67. Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H. (2023). Self-Instruct: Aligning Language Models with Self-Generated Instructions. arXiv preprint. https://arxiv.org/abs/2212.10560
68. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., and Wen, J.-R. (2024). A Survey on Large Language Model based Autonomous Agents. arXiv preprint. https://arxiv.org/abs/2308.11432
69. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv preprint. https://arxiv.org/abs/2210.03629
70. Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., Zhang, H., Gonzalez, J. E., and Stoica, I. (2023). Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. arXiv preprint. https://arxiv.org/abs/2306.05685
71. Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O. (2023). LIMA: Less Is More for Alignment. arXiv preprint. https://arxiv.org/abs/2305.11206