Building LLM Powered Applications AI(20) 

Building LLM Powered Applications
https://learning.oreilly.com/library/view/building-llm-powered/9781835462317/

# References
## 1 Introduction to Large Language Models
Attention is all you need: 1706.03762.pdf (arxiv.org)
Possible End of Humanity from AI? Geoffrey Hinton at MIT Technology Review’s EmTech Digital: https://www.youtube.com/watch?v=sitHS6UDMJc&t=594s&ab_channel=JosephRaczynski
The Glue Benchmark: https://gluebenchmark.com/
TruthfulQA: https://paperswithcode.com/dataset/truthfulqa
Hugging Face Open LLM Leaderboard: https://huggingface.co/spaces/optimum/llm-perf-leaderboard
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge: https://arxiv.org/abs/1803.05457

##  2 LLMs for AI-Powered Applications
LangChain repository: https://github.com/langchain-ai/langchain
Semantic Kernel documentation: https://learn.microsoft.com/en-us/semantic-kernel/get-started/supported-languages
Copilot stack: https://build.microsoft.com/en-US/sessions/bb8f9d99-0c47-404f-8212-a85fffd3a59d?source=/speakers/ef864919-5fd1-4215-b611-61035a19db6b
The Copilot system: https://www.youtube.com/watch?v=E5g20qmeKpg

## 3 Choosing an LLM for Your Application
GPT-4 Technical Report. https://cdn.openai.com/papers/gpt-4.pdf
Train short, test long: attention with linear biases enables input length extrapolation. https://arxiv.org/pdf/2108.12409.pdf
Constitutional AI: Harmlessness from AI Feedback. https://arxiv.org/abs/2212.08073
Hugging Face Inference Endpoint. https://huggingface.co/docs/inference-endpoints/index
Hugging Face Inference Endpoint Pricing. https://huggingface.co/docs/inference-endpoints/pricing
Model Card for BioMedLM 2.7B. https://huggingface.co/stanford-crfm/BioMedLM
PaLM 2 Technical Report. https://ai.google/static/documents/palm2techreport.pdf
Solving Quantitative Reasoning Problems with Language Models. https://arxiv.org/abs/2206.14858
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. https://arxiv.org/abs/2306.05685
## 4 Prompt Engineering
ReAct approach: https://arxiv.org/abs/2210.03629
What is prompt engineering?: https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-prompt-engineering
Prompt engineering techniques: https://blog.mrsharm.com/prompt-engineering-guide/
Prompt engineering principles: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions
Recency bias: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#repeat-instructions-at-the-end
Large Language Model Prompt Engineering for Complex Summarization: https://devblogs.microsoft.com/ise/2023/06/27/gpt-summary-prompt-engineering/
Language Models are Few-Shot Learners: https://arxiv.org/pdf/2005.14165.pdf
IMDb dataset: https://www.kaggle.com/datasets/yasserh/imdb-movie-ratings-sentiment-analysis/code
ReAct: https://arxiv.org/abs/2210.03629
Chain of Thought Prompting Elicits Reasoning in Large Language Models: https://arxiv.org/abs/2201.11903

## 5 Embedding LLMs within Your Applications
LangChain’s integration with OpenAI – https://python.langchain.com/docs/integrations/llms/openai
LangChain’s prompt templates – https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/
LangChain’s vector stores – https://python.langchain.com/docs/integrations/vectorstores/
FAISS index – https://faiss.ai/
LangChain’s chains – https://python.langchain.com/docs/modules/chains/
ReAct approach – https://arxiv.org/abs/2210.03629
LangChain’s agents – https://python.langchain.com/docs/modules/agents/agent_types/
Hugging Face documentation – https://huggingface.co/docs
LangChain Expression Language (LCEL) – https://python.langchain.com/docs/expression_language/
LangChain stable version – https://blog.langchain.dev/langchain-v0-1-0/

## 6 Building Conversational Applications
Example of a context-aware chatbot. https://github.com/shashankdeshpande/langchain-chatbot/blob/master/pages/2_%E2%AD%90_context_aware_chatbot.py
Knowledge base for the AI travel assistant. https://www.minube.net/guides/italy
LangChain repository. https://github.com/langchain-ai

## 7 Search and Recommendation Engines with LLMs
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). https://arxiv.org/abs/2203.13366
LangChain’s blog about featurestores. https://blog.langchain.dev/feature-stores-and-llms/
Feast. https://docs.feast.dev/
Tecton. https://www.tecton.ai/
FeatureForm. https://www.featureform.com/
Azure Machine Learning feature store. https://learn.microsoft.com/en-us/azure/machine-learning/concept-what-is-managed-feature-store?view=azureml-api-2

## 8 Using LLMs with Structured Data
Chinook Database: https://github.com/lerocha/chinook-database/tree/master/ChinookDatabase/DataSources
LangChain File system tool: https://python.langchain.com/docs/integrations/tools/filesystem
LangChain Python REPL tool: https://python.langchain.com/docs/integrations/toolkits/python

## 9 Working with Code
The open-source version of the Code Interpreter API: https://github.com/shroominic/codeinterpreter-api
StarCoder: https://huggingface.co/blog/starcoder
The LangChain agent for the Python REPL: https://python.langchain.com/docs/integrations/toolkits/python
A LangChain blog about the Code Interpreter API: https://blog.langchain.dev/code-interpreter-api/
The Titanic dataset: https://www.kaggle.com/datasets/brendan45774/test-file
The HF Inference Endpoint: https://huggingface.co/docs/inference-endpoints/index
The CodeLlama model card: https://huggingface.co/codellama/CodeLlama-7b-hf
Code Llama: Open Foundation Models for Code, Rozière. B., et al (2023): https://arxiv.org/abs/2308.12950
The Falcon LLM model card: https://huggingface.co/tiiuae/falcon-7b-instruct
The StarCoder model card: https://huggingface.co/bigcode/starcoder

## 10 Building Multimodal Applications with LLMs
Source code for YouTube tools: https://github.com/venuv/langchain_yt_tools
LangChain YouTube tool: https://python.langchain.com/docs/integrations/tools/youtube
LangChain AzureCognitiveServicesToolkit: https://python.langchain.com/docs/integrations/toolkits/azure_cognitive_services
## 11 Fine-Tuning Large Language Models
Training dataset: https://huggingface.co/datasets/imdb
HF AutoTrain: https://huggingface.co/docs/autotrain/index
BERT paper: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2019, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://arxiv.org/abs/1810.04805

## 12 Responsible AI
Reducing Gender Bias Amplification using Corpus-level Constraints: https://browse.arxiv.org/pdf/1707.09457.pdf
ChatGPT racist and sexist outputs: https://twitter.com/spiantado/status/1599462375887114240
GitHub repository for an aligned dataset: https://github.com/Zjh-819/LLMDataHub#general-open-access-datasets-for-alignment-
AI Act: https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf
Prompt hijacking: https://arxiv.org/pdf/2211.09527.pdf
AI Act: https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
Blueprint for an AI Bill of Rights: https://www.whitehouse.gov/ostp/ai-bill-of-rights/

## 13 Emerging Trends and Innovations
GPT-4V(ision) System Card: GPTV_System_Card.pdf (openai.com)
AutoGen paper: Qingyun Wu et al., 2023, AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation: https://arxiv.org/pdf/2308.08155.pdf
AutoGen GitHub: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb
DALL-E 3: James Betker, Improving Image Generation with Better Captions: https://cdn.openai.com/papers/dall-e-3.pdf
Notion AI: https://www.notion.so/product/ai
Coca-Cola and Bain partnership: https://www.coca-colacompany.com/media-center/coca-cola-invites-digital-artists-to-create-real-magic-using-new-ai-platform
Malbek and ChatGPT: https://www.malbek.io/news/chat-gpt-malbek-unveils-generative-ai-functionality
Microsoft Copilot: https://www.microsoft.com/en-us/microsoft-365/blog/2023/09/21/announcing-microsoft-365-copilot-general-availability-and-microsoft-365-chat/