Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/aipengineer/awesome-ai-engineering-reads

Essential reads for every AI engineer interested in building AI apps.
https://github.com/aipengineer/awesome-ai-engineering-reads
List: awesome-ai-engineering-reads
ai ai-engineer ai-engineering aiengineer aiengineering awesome awesome-list awesome-lists
Last synced: about 1 month ago
JSON representation
Essential reads for every AI engineer interested in building AI apps.
Host: GitHub
URL: https://github.com/aipengineer/awesome-ai-engineering-reads
Owner: aipengineer
Created: 2023-11-25T00:30:59.000Z (11 months ago)
Default Branch: main
Last Pushed: 2024-02-29T16:10:17.000Z (8 months ago)
Last Synced: 2024-09-25T18:01:09.729Z (about 1 month ago)
Topics: ai, ai-engineer, ai-engineering, aiengineer, aiengineering, awesome, awesome-list, awesome-lists
Homepage: https://aipengineer.substack.com
Size: 117 KB
Stars: 1
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-ai-engineering-reads - Essential reads for every AI engineer interested in building AI apps. (Other Lists / PowerShell Lists)
README

        # Awesome AI Engineer Reads

[🏗️ Building and Deploying LLMs](#building-and-deploying-llms)

[:balance_scale: Evaluating LLM + Retrieval](#evaluating-llm-plus-retrieval)

[:mag: LLM + Retrieval](#llm-and-retrieval)

[:computer: LLM Applications](#llm-applications)

[:brain: LLM Architectures and Models](#llm-architectures-and-models)

[:briefcase: LLM Business](#llm-business)

[:bar_chart: LLM Data](#llm-data)

[:rocket: LLM Deployment ](#llm-deployment)

[:jigsaw: LLM Embeddings](#llm-embeddings)

[:wrench: LLM Engineering](#llm-engineering)

[:trophy: LLM Engineering Best Practices](#llm-engineering-best-practices)

[:balance_scale: LLM Ethics and Governance](#llm-ethics-and-governance)

[:microscope: LLM Evaluation](#llm-evaluation)

[:ticket: LLM Events](#llm-events)

[:exploding_head: LLM Hype](#llm-hype)

[:thinking: LLM Inference](#llm-inference)

[:building_construction: LLM Infrastructure](#llm-infrastructure)

[:loudspeaker: LLM Marketing](#llm-marketing)

[:newspaper: LLM Newsletters](#llm-newsletters)

[:1234: LLM Numbers](#llm-numbers)

[:eyes: LLM Observability](#llm-observability)

[:speech_balloon: LLM Opinions and Critiques](#llm-opinions-and-critiques)

[:pencil: LLM Prompting](#llm-prompting)

[:notebook_with_decorative_cover: LLM Research and Publications](#llm-research-and-publications)

[:fishing_pole_and_fish: LLM Retriever Models](#llm-retriever-models)

[:moneybag: LLM Startups and Funding](#llm-startups-and-funding)

[:green_book: LLM Tutorials and Courses](#llm-tutorials-and-courses)

[:unlock: Open LLM Models](#open-llm-models)

[:wrench: Open LLM Tools](#open-llm-tools)

[:desktop_computer: Self-Hosted LLMs](#self-hosted-llms)

[:hammer_and_wrench: Tools and Frameworks](#tools-and-frameworks)

[:weight_lifting: Training and Fine-tuning LLMs](#training-and-fine-tuning-llms)

[:grey_question: Uncategorized](#uncategorized)

## Building and Deploying LLMs

- [Scaling Kubernetes to 7,500 nodes](https://openai.com/research/scaling-kubernetes-to-7500-nodes)

- [All the Hard Stuff Nobody Talks About when Building Products with LLMs](https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm)

- [All the Hard Stuff Nobody Talks About when Building Products with LLMs](https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm)

- [Building LLM applications for production](https://huyenchip.com/2023/04/11/llm-engineering.html)  

- [Efficiently Scaling and Deploying LLMs](https://home.mlops.communiy/home/videos/efficiently-scaling-and-deploying-llms)

- [LLMs in Production - Part III](https://home.mlops.community/public/events/llms-in-production-part-iii-2023-10-03)

## Training and Fine-tuning LLMs

- [How to train your own Large Language Models](https://blog.replit.com/llm-training)  

- [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556)

- [Opt-175B Logbook](https://github.com/facebookresearch/metaseq/blob/main/projects/OPT/chronicles/OPT175B_Logbook.pdf)

- [Fine-Tuning LLMs: Best Practices and When to Go Small](https://home.mlops.community/home/videos/fine-tuning-llms-best-practices-and-when-to-go-small-2023-06-02)  

- [Finetuning Large Language Models](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/)

- [GPT-3.5 Turbo fine-tuning and API updates](https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates)

- [Fine-tuning in Your Voice Webinar](https://www.youtube.com/watch?v=MJm49TAyz5w)

## LLM Architectures and Models

- [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)

- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)   

- [Introducing Code Llama, a state-of-the-art large language model for coding](https://ai.meta.com/blog/code-llama-large-language-model-coding/)

- [Spread Your Wings: Falcon 180B is here](https://huggingface.co/blog/falcon-180b)

- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/pdf/2211.05100.pdf?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

## Tools and Frameworks

- [LangChain: Enabling LLMs to Use Tools](https://drive.google.com/file/d/1Z9wxwZRG8JOMkUfwDw7fD8sbijQ32J7W/view)  

- [Langchain Tutorials](https://github.com/gkamradt/langchain-tutorials)

- [LangChain cookbook](https://github.com/langchain-ai/langchain/tree/master/cookbook)

- [fairseq2](https://github.com/facebookresearch/fairseq2)

- [seamless_communication](https://github.com/facebookresearch/seamless_communication)

## LLM Applications  

- [Automatic Generation of Visualizations and Infographics with LLMs](https://microsoft.github.io/lida/)

- [Introducing AudioCraft: A Generative AI Tool For Audio and Music](https://about.fb.com/news/2023/08/audiocraft-generative-ai-for-music-and-audio/)

- [An example of LLM prompting for programming](https://martinfowler.com/articles/2023-chatgpt-xu-hao.html)   

- [Human-centric & Coherent Whole Program Synthesis aka your own personal junior developer](https://github.com/smol-ai/developer)

- [GPT Engineer](https://github.com/AntonOsika/gpt-engineer/)

## LLM Evaluation  

- [Holistic Evaluation of Language Models](https://github.com/stanford-crfm/helm) 

- [chatgpt-evaluation-01-2023](https://github.com/CLARIN-PL/chatgpt-evaluation-01-2023)

- [Evaluating chatGPT](https://ehudreiter.com/2023/04/04/evaluating-chatgpt/)

- [PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://arxiv.org/pdf/2306.04528.pdf)

## LLM Prompting

- [Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/) 

- ["Prompt injection explained, with video, slides, and a transcript"](https://simonwillison.net/2023/May/2/prompt-injection-explained/)

- [Delimiters won’t save you from prompt injection](https://simonwillison.net/2023/May/11/delimiters-wont-save-you/)

- [You probably don't know how to do Prompt Engineering](https://gist.github.com/Hellisotherpeople/45c619ee22aac6865ca4bb328eb58faf)

- [ChatGPT Prompt Engineering for Developers](https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/2/guidelines)

- [Learn Prompting](https://learnprompting.org/docs/category/-basics)  

## LLM Research and Publications

- [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)

- [Challenges and Applications of Large Language Models](https://arxiv.org/abs/2307.10169) 

- [Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409)

- [Multimodal Foundation Models: From Specialists to General-Purpose Assistants](https://arxiv.org/pdf/2309.10020.pdf)

- [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

- [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)  

## LLM Embeddings

- [FlagEmbedding](https://huggingface.co/BAAI/bge-large-en-v1.5)

- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://github.com/xlang-ai/instructor-embedding)

- [OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings?](https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9)  

- [Getting creative with embeddings](https://wattenberger.com/thoughts/yay-embeddings-math)

- [What are embeddings?](https://vickiboykis.com/what_are_embeddings/)

## LLM Retriever Models

- [Vector Databases and Large Language Models](https://drive.google.com/file/d/1SWUJEM8ZtsOPqrqW3b-Nn8HooPe9mgv8/view)

- [Large Language Models with Semantic Search](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search/)  

- [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)  

- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://arxiv.org/pdf/2310.08319.pdf)

## LLM plus Retrieval

- [Making Retrieval Augmented Generation Better with @jamesbriggs](https://www.youtube.com/watch?v=Q-uEhJMu3ak)

- [Harnessing Retrieval Augmented Generation With Langchain](https://betterprogramming.pub/harnessing-retrieval-augmented-generation-with-langchain-2eae65926e82)

- [Knowledge Retrieval Architecture for LLM’s (2023)](https://mattboegner.com/knowledge-retrieval-architecture-for-llms/)  

- [How do domain-specific chatbots work? An Overview of Retrieval Augmented Generation (RAG)](https://scriv.ai/guides/retrieval-augmented-generation-overview/)

- [LangChain "Advanced Retrieval" Webinar](https://www.crowdcast.io/c/kqz7nl8nps42)

- [TWIML-RAG - a TWIML generative_ai community project.](https://github.com/TWIML/TWIML-RAG)

## Evaluating LLM plus Retrieval

- [Evaluation & Hallucination Detection for Abstractive Summaries](https://eugeneyan.com/writing/abstractive/) 

- [Building And Troubleshooting An Advanced LLM Query Engine](https://www.youtube.com/watch?v=_zDDErOaUqc)

- [Advanced RAG 02 - Parent Document Retriever](https://www.youtube.com/watch?v=wQEl0GGxPcM)

## LLM Inference

- [Easy-to-use headless React Hooks to run LLMs in the browser with WebGPU. As simple as useLLM().](https://github.com/r2d4/react-llm)

- [Inference Experiments with LLaMA v2 7b](https://github.com/djliden/inference-experiments/tree/main/llama2)  

- [OpenAI's Code Interpreter in your terminal, running locally](https://github.com/KillianLucas/open-interpreter)

- [Discover, download, and run local LLMs](https://lmstudio.ai/)

## LLM Data

- [Want High Performing LLMs? Hint: It is All About Your Data](https://home.mlops.community/home/videos/want-high-performing-llms-hint-it-is-all-about-your-data)

- [How LlamaIndex Can Bring the Power of LLM's to Your Data](https://drive.google.com/file/d/1qo_DqCilZdtCyQE1dpdJeJnav0fgunax/view) 

- [How to Create Custom Datasets To Train Llama-2](https://www.youtube.com/watch?v=z2QE12p3kMM)

- [Data Copilot](https://github.com/Modulos/data_copilot)

## LLM Observability  

- [AI Observability & Evaluation - Evaluate, troubleshoot, and fine tune your LLM, CV, and NLP models in a notebook.](https://github.com/Arize-ai/phoenix)

## LLM Opinions and Critiques  

- [Why Chatbots Are Not the Future](https://wattenberger.com/thoughts/boo-chatbots)

## LLM Tutorials and Courses  

- [Finetuning Large Language Models](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/)

- [Large Language Models with Semantic Search](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search/)

- [How Business Thinkers Can Start Building AI Plugins With Semantic Kernel](https://www.deeplearning.ai/short-courses/microsoft-semantic-kernel/)

- [LLM Bootcamp - Spring 2023](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/)

- [LLM Bootcamp - Spring 2023](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/)

- [Generative AI for Beginners - A Course](https://github.com/microsoft/generative-ai-for-beginners/tree/main)

- [Artificial Intelligence for Beginners - A Curriculum](https://github.com/microsoft/ai-for-beginners)

- [Introduction to Deep Learning](https://sebastianraschka.com/blog/2021/dl-course.html#l19-self-attention-and-transformer-networks)  

## LLM Engineering

- [Reflections on AI Engineer Summit 2023](https://eugeneyan.com/writing/aieng-reflections/)

- [AI Engineer Summit - Building Blocks for LLM Systems & Products](https://eugeneyan.com/speaking/ai-eng-summit/)

## LLM Engineering Best Practices  

- [All the Hard Stuff Nobody Talks About when Building Products with LLMs](https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm)

- [Building Defensible Products with LLMs](https://drive.google.com/file/d/1AOX4urZL1ac20MhnvEY2saDUmyR2vAOD/view)  

- [What Is ChatGPT Doing ... and Why Does It Work?](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/)  

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://arxiv.org/abs/2103.11251)

## Open LLM Models

- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/pdf/2211.05100.pdf?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)  

## Open LLM Tools  

- [LangChain: Enabling LLMs to Use Tools](https://drive.google.com/file/d/1Z9wxwZRG8JOMkUfwDw7fD8sbijQ32J7W/view)

- [Langchain Tutorials](https://github.com/gkamradt/langchain-tutorials)

- [LangChain cookbook](https://github.com/langchain-ai/langchain/tree/master/cookbook)

- [fairseq2](https://github.com/facebookresearch/fairseq2)

- [seamless_communication](https://github.com/facebookresearch/seamless_communication)

## Self-Hosted LLMs

- [Why host your own LLM?](http://marble.onl/posts/why_host_your_own_llm.html)   

- [How is LLaMa.cpp possible?](https://finbarr.ca/how-is-llama-cpp-possible/)

- [Discover, download, and run local LLMs](https://lmstudio.ai/)

## LLM Deployment

- [Deploying AI-driven apps on Vercel](https://vercel.com/blog/deploying-ai-applications)  

- [Solving the Last Mile Problem of Foundation Models with Data-Centric AI](https://home.mlops.community/home/videos/solving-the-last-mile-problem-of-foundation-models-with-data-centric-ai)

- [Taking LangChain Apps to Production with LangChain-serve](https://drive.google.com/file/d/14AY8DUlgvaLy73XOffYuTrqaweA2NH9w/view)

## LLM Infrastructure 

- [Age of Industrialized AI](https://docs.google.com/presentation/d/1yOwISoa-ujVK5ImC1aP2JoJslVWAe4GsQlih29TTQ5g/edit#slide=id.g2078c9f2667_0_62)

- [DevTools for Large Language Models: Unlocking the Future of AI-Driven Applications](https://docs.google.com/presentation/d/1kML0Q7CnRPp1eEByGqziGOpvGlcQD8j-/edit#slide=id.p1)

- [Large Language Model at Scale](https://home.mlops.community/home/videos/large-language-model-at-scale)

## LLM Events

- ["AI Agents Hackathon Develop your own AI agent within 48 hours!"](https://lablab.ai/event/ai-agents-hackathon)

## LLM Numbers 

- [Numbers every LLM Developer should know](https://github.com/ray-project/llm-numbers)

## LLM Hype  

- [Anti-hype LLM reading list](https://gist.github.com/veekaybee/be375ab33085102f9027853128dc5f0e)

- [ChatGPT Resources](https://gist.github.com/veekaybee/6f8885e9906aa9c5408ebe5c7e870698)

## LLM Newsletters

- [✨ Flashier Attention, 🤐 Gzip Classifiers](https://nlpnewsletter.substack.com/p/flashier-attention-gzip-classifiers)

## LLM Ethics and Governance   

- [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073?utm_source=www.turingpost.com&utm_medium=newsletter&utm_campaign=where-are-you-in-fmops-infrastructure-stack-tell-us)

- [A guidance language for controlling large language models.](https://github.com/guidance-ai/guidance)

## LLM Startups and Funding  

- [The AI Startup Litmus Test](https://www.nfx.com/post/ai-startup-litmus-test)

- [Generative AI Strategy](https://huyenchip.com/2023/06/07/generative-ai-strategy.html) 

- [Lessons from 139 YC AI startups (S23)](https://www.reddit.com/r/ycombinator/comments/16h9lxu/lessons_from_139_yc_ai_startups_s23/?share_id=FzwJ8S6GOz1SkuG9WDY8y&utm_content=2&utm_medium=ios_app&utm_name=ioscss&utm_source=share&utm_term=1)

## LLM Business  

- [2023: The State of Generative AI in the Enterprise](https://menlovc.com/2023-the-state-of-generative-ai-in-the-enterprise-report/)  

- [Takeaways & lessons from 250k+ LLM calls on 100k corporate docs](https://www.credal.ai/blog/takeaways-from-using-llms-on-corporate-documents)

## LLM Marketing  

- [Your Success with Generative AI May Come Down to These UX Decisions](https://www.emcap.com/thoughts/your-success-with-generative-ai-may-come-down-to-these-ux-decisions/)

## Uncategorized

	https://github.com/yeagerai/yeagerai-agent

	https://github.com/homanp/superagent

	https://github.com/homanp/langchain-ui

	https://github.com/0xpayne/gpt-migrate

	https://github.com/shinework/photoshot

	https://arxiv.org/abs/2201.11903

	https://arxiv.org/abs/2210.03629

	https://arxiv.org/abs/2303.11366

	https://arxiv.org/abs/2305.10601

	https://news.ycombinator.com/item?id=36645575

	https://github.com/prefecthq/marvin

	https://github.com/eth-sri/lmql

	https://arxiv.org/pdf/2212.06094.pdf

	https://github.com/mlc-ai/web-llm

	https://github.com/Atome-FE/llama-node

	https://github.com/go-skynet/LocalAI

	https://localai.io

	https://www.cursor.so/blog/llama-inference

	https://www.geoffreylitt.com/2023/03/25/llm-end-user-programming.html

	https://every.to/chain-of-thought/what-comes-after-saas

	https://github.com/axilla-io/axgen

	https://www.axilla.io/

	https://github.com/f/awesome-chatgpt-prompts/blob/main/README.md

	https://magrawala.substack.com/p/unpredictable-black-boxes-are-terrible

	https://dl.acm.org/doi/10.1145/267505.267514

	https://www.youtube.com/@gklitt/videos

	https://www.inkandswitch.com

	https://idl.cs.washington.edu/files/2019-AgencyPlusAutomation-PNAS.pdf

	https://simonwillison.net/2023/Mar/27/ai-enhanced-development/

	https://www.robinsloan.com/notes/home-cooked-app/

	https://dl.acm.org/doi/10.1145/2593882.2593896

	https://wattenberger.com/thoughts/boo-chatbots

	https://www.geoffreylitt.com/2023/07/25/building-personal-tools-on-the-fly-with-llms.html

	https://web.mit.edu/6.031/www/sp22/

	https://www.youtube.com/watch?v=bJ3i4K3hefI

	https://github.com/tianlinxu312/Everything-about-LLMs

 	https://arxiv.org/abs/2311.04205

 	https://arxiv.org/abs/2309.04269

 	https://arxiv.org/abs/2310.11511

  	https://arxiv.org/pdf/2310.07064

   	https://arxiv.org/abs/2309.15217

    	https://arxiv.org/abs/2304.08354

    	https://arxiv.org/abs/2203.11171

     	https://arxiv.org/abs/2310.06692

     	https://arxiv.org/abs/2310.05029

     	https://arxiv.org/abs/2005.11401

      	https://arxiv.org/abs/2212.10071

       https://arxiv.org/abs/2301.12726

       https://arxiv.org/abs/2305.01879

       https://arxiv.org/abs/2305.02301

       https://arxiv.org/abs/2212.00193

       https://arxiv.org/abs/2305.13888

       https://arxiv.org/abs/2306.09299

       https://arxiv.org/abs/2207.00112

       https://arxiv.org/abs/2307.00526

       https://arxiv.org/abs/2106.09685

       https://arxiv.org/abs/2210.07558

       https://arxiv.org/abs/2311.12023

       https://arxiv.org/abs/2311.11696

       https://arxiv.org/abs/2311.09179

       https://arxiv.org/abs/2311.08598

       https://arxiv.org/abs/2311.05556

       https://cset.georgetown.edu/publication/techniques-to-make-large-language-models-smaller-an-explainer

       https://arxiv.org/abs/2304.01089

       https://arxiv.org/abs/2304.07493

       https://arxiv.org/abs/2304.09145

       https://arxiv.org/pdf/2306.02272

       https://arxiv.org/abs/2307.09782

       https://arxiv.org/abs/2303.08302

       https://arxiv.org/abs/2306.07629

       https://arxiv.org/abs/2307.13304

       https://arxiv.org/abs/2308.15987v1

       https://arxiv.org/abs/2309.01885

       https://arxiv.org/abs/2309.02784

       https://arxiv.org/abs/2309.05516

       https://arxiv.org/abs/2308.13137

       https://arxiv.org/abs/2306.08543

       https://arxiv.org/abs/2306.13649

       https://arxiv.org/abs/2305.14864

       https://arxiv.org/abs/2301.00234

       https://arxiv.org/abs/2301.11916

       https://arxiv.org/abs/2212.10670

       https://arxiv.org/abs/2210.06726

       https://arxiv.org/abs/2212.08410

       https://arxiv.org/abs/2305.14152

       https://arxiv.org/abs/2305.14314

       https://arxiv.org/abs/2210.17323

       https://arxiv.org/abs/2306.00978

       https://www.ben-evans.com/benedictevans/2023/10/5/unbundling-ai

       https://www.coatue.com/blog/perspective/ai-the-coming-revolution-2023

       https://arxiv.org/pdf/2308.07633

       https://arxiv.org/abs/2301.00774

       https://arxiv.org/abs/2305.18403

       https://arxiv.org/abs/2306.11695

       https://arxiv.org/abs/2305.11627

       https://arxiv.org/abs/2305.17888

       https://arxiv.org/abs/2305.14152

       https://arxiv.org/abs/2206.09557

       https://arxiv.org/abs/2208.07339

       https://arxiv.org/abs/2206.09557

       https://arxiv.org/abs/2208.07339

       https://arxiv.org/abs/2206.01861

       https://arxiv.org/abs/2211.10438

       https://arxiv.org/abs/2305.14152

       https://arxiv.org/abs/2305.17888

       https://arxiv.org/abs/2306.03078

       https://arxiv.org/pdf/2311.10122

       https://arxiv.org/pdf/2311.10093

       https://arxiv.org/pdf/2311.08263

       https://arxiv.org/pdf/2311.07575

       https://arxiv.org/pdf/2311.06783

       https://arxiv.org/pdf/2311.09210v1

       https://arxiv.org/pdf/2311.10709

       https://arxiv.org/pdf/2311.07361

       https://arxiv.org/pdf/2311.07989

       https://arxiv.org/pdf/2311.02462

       https://arxiv.org/abs/2311.05232

       https://arxiv.org/pdf/2311.03285v1

       https://arxiv.org/pdf/2311.05556

       https://arxiv.org/pdf/2311.05437

       https://arxiv.org/pdf/2311.05348

       https://arxiv.org/pdf/2311.04257

       https://arxiv.org/pdf/2311.04219

       https://arxiv.org/pdf/2311.03356

       https://arxiv.org/pdf/2311.05657

       https://arxiv.org/pdf/2311.05997

       https://arxiv.org/pdf/2311.04254

       https://arxiv.org/pdf/2311.03301

       https://arxiv.org/pdf/2311.04400

       https://arxiv.org/pdf/2311.04145

       [Universal Language Model Fine-tuning for Text Classification](https://arxiv.org/pdf/1801.06146.pdf)

       https://idratherbewriting.com/blog/writing-full-length-articles-with-claude-ai

       https://www.oliverwyman.com/our-expertise/insights/2023/nov/impact-of-artificial-intelligence-in-financial-services.html

       https://www.youtube.com/watch?v=zjkBMFhNj_g

       https://towardsdatascience.com/recreating-andrej-karpathys-weekend-project-a-movie-search-engine-9b270d7a92e4

       https://streamlit.io/generative-ai

       https://www.youtube.com/watch?app=desktop&v=1RxOYLa69Vw

       https://www.bentoml.com/blog/announcing-open-llm-an-open-source-platform-for-running-large-language-models-in-production

       https://www.pinecone.io/learn/chunking-strategies/

       https://blog.llamaindex.ai/evaluating-the-ideal-chunk-size-for-a-rag-system-using-llamaindex-6207e5d3fec5

       https://amatriain.net/blog/hallucinations

       https://amatriain.net/blog/PromptEngineering

       https://realpython.com/chromadb-vector-database/

       https://github.com/zilliztech/VectorDBBench

       https://qdrant.tech/benchmarks/

       https://docs.ragas.io/en/latest/index.html

       https://towardsdatascience.com/10-ways-to-improve-the-performance-of-retrieval-augmented-generation-systems-5fa2cee7cd5c

       https://medium.com/@kelvin.lu.au/disadvantages-of-rag-5024692f2c53

       https://towardsdatascience.com/the-untold-side-of-rag-addressing-its-challenges-in-domain-specific-searches-808956e3ecc8

       https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1

       https://github.com/BuilderIO/gpt-crawler

       https://www.secondstate.io/articles/mistral-7b-instruct-v0.1/

       https://github.com/stas00/ml-engineering/tree/master

       https://arxiv.org/abs/2303.12712

       https://arxiv.org/abs/2203.15556

       https://arxiv.org/abs/2309.00267

       https://github.com/karpathy/llama2.c/blob/master/run.c

       https://github.com/ggerganov/llama.cpp

       https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/

       https://arxiv.org/abs/2203.02155

       https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

       https://arxiv.org/abs/1706.03762

       https://old.reddit.com/r/LocalLLaMA/comments/1atquor/im_open_sourcing_our_rag_backend_our_cqh_gql_chs/

       https://ravinkumar.com/GenAiGuidebook/

       https://towardsdatascience.com/advanced-retrieval-augmented-generation-from-theory-to-llamaindex-implementation-4de1464a9930

       https://srush.github.io/annotated-mamba/hard.html