diff --git a/docs/docs/dspy-usecases.md b/docs/docs/dspy-usecases.md index 3ae7ec499..4d982f889 100644 --- a/docs/docs/dspy-usecases.md +++ b/docs/docs/dspy-usecases.md @@ -12,135 +12,137 @@ This list is ever expanding and highly incomplete (WIP)! We'll be adding a bunch 4. [Providers with DSPy support](#a-few-providers-integrations-and-related-blog-releases) 5. [Blogs & Videos on using DSPy](#a-few-blogs--videos-on-using-dspy) + ## A Few Company Use Cases -| **Name** | **Use Cases** | -| --------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **[JetBlue](https://www.jetblue.com/)** | Multiple chatbot use cases. [Blog](https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy) | -| **[Replit](https://replit.com/)** | Synthesize diffs using code LLMs using a DSPy pipeline. [Blog](https://blog.replit.com/code-repair) | -| **[Databricks](https://www.databricks.com/)** | Research, products, and customer solutions around LM Judges, RAG, classification, and other applications. [Blog](https://www.databricks.com/blog/dspy-databricks) [Blog II](https://www.databricks.com/customers/ddi) | -| **[Sephora](https://www.sephora.com/)** | Undisclosed agent usecases; perspectives shared in [DAIS Session](https://www.youtube.com/watch?v=D2HurSldDkE). | -| **[Zoro UK](https://www.zoro.co.uk/)** | E-commerce applications around structured shopping. [Portkey Session](https://www.youtube.com/watch?v=_vGKSc1tekE) | -| **[VMware](https://www.vmware.com/)** | RAG and other prompt optimization applications. [Interview in The Register.](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) [Business Insider.](https://www.businessinsider.com/chaptgpt-large-language-model-ai-prompt-engineering-automated-optimizer-2024-3) | -| **[Haize Labs](https://www.haizelabs.com/)** | Automated red-teaming for LLMs. [Blog](https://blog.haizelabs.com/posts/dspy/) | -| **[Plastic Labs](https://www.plasticlabs.ai/)** | Different pipelines within Honcho. [Blog](https://blog.plasticlabs.ai/blog/User-State-is-State-of-the-Art) | -| **[PingCAP](https://pingcap.com/)** | Building a knowledge graph. [Article](https://www.pingcap.com/article/building-a-graphrag-from-wikipedia-page-using-dspy-openai-and-tidb-vector-database/) | -| **[Salomatic](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system)** | Enriching medical reports using DSPy. [Blog](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system) | -| **[Truelaw](https://www.youtube.com/watch?v=O0F3RAWZNfM)** | How Truelaw builds bespoke LLM pipelines for law firms using DSPy. [Podcast](https://www.youtube.com/watch?v=O0F3RAWZNfM) | -| **[Moody's](https://www.moodys.com/)** | Leveraging DSPy to optimize RAG systems, LLM-as-a-Judge, and agentic systems for financial workflows. | -| **[Normal Computing](https://www.normalcomputing.com/)** | Translate specs from chip companies from English to intermediate formal languages | -| **[Procure.FYI](https://www.procure.fyi/)** | Process messy, publicly available technology spending and pricing data via DSPy. | -| **[RadiantLogic](https://www.radiantlogic.com/)** | AI Data Assistant. DSPy is used for the agent that routes the query, the context extraction module, the text-to-sql conversion engine, and the table summarization module. | -| **[Hyperlint](https://hyperlint.com)** | Uses DSPy to generate technical documentation. DSPy helps to fetch relevant information and synthesize that into tutorials. | -| **[Starops](https://staropshq.com/) & [Saya](https://heysaya.ai/)** | Building research documents given a user's corpus. Generate prompts to create more articles from example articles. | -| **[Tessel AI](https://tesselai.com/)** | Enhancing human-machine interaction with data use cases. | -| **[Dicer.ai](https://dicer.ai/)** | Uses DSPy for marketing AI to get the most from their paid ads. | -| **[Howie](https://howie.ai)** | Using DSPy to automate meeting scheduling through email. | -| **[Isoform.ai](https://isoform.ai)** | Building custom integrations using DSPy. | -| **[Trampoline AI](https://trampoline.ai)** | Uses DSPy to power their data-augmentation and LM pipelines. | +| **Name** | **Use Cases** | +|---|---| +| **[JetBlue](https://www.jetblue.com/)** | Multiple chatbot use cases. [Blog](https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy) | +| **[Replit](https://replit.com/)** | Synthesize diffs using code LLMs using a DSPy pipeline. [Blog](https://blog.replit.com/code-repair) | +| **[Databricks](https://www.databricks.com/)** | Research, products, and customer solutions around LM Judges, RAG, classification, and other applications. [Blog](https://www.databricks.com/blog/dspy-databricks) [Blog II](https://www.databricks.com/customers/ddi) | +| **[Sephora](https://www.sephora.com/)** | Undisclosed agent usecases; perspectives shared in [DAIS Session](https://www.youtube.com/watch?v=D2HurSldDkE). | +| **[Zoro UK](https://www.zoro.co.uk/)** | E-commerce applications around structured shopping. [Portkey Session](https://www.youtube.com/watch?v=_vGKSc1tekE) | +| **[VMware](https://www.vmware.com/)** | RAG and other prompt optimization applications. [Interview in The Register.](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) [Business Insider.](https://www.businessinsider.com/chaptgpt-large-language-model-ai-prompt-engineering-automated-optimizer-2024-3) | +| **[Haize Labs](https://www.haizelabs.com/)** | Automated red-teaming for LLMs. [Blog](https://blog.haizelabs.com/posts/dspy/) | +| **[Plastic Labs](https://www.plasticlabs.ai/)** | Different pipelines within Honcho. [Blog](https://blog.plasticlabs.ai/blog/User-State-is-State-of-the-Art) | +| **[PingCAP](https://pingcap.com/)** | Building a knowledge graph. [Article](https://www.pingcap.com/article/building-a-graphrag-from-wikipedia-page-using-dspy-openai-and-tidb-vector-database/) | +| **[Salomatic](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system)** | Enriching medical reports using DSPy. [Blog](https://langtrace.ai/blog/case-study-how-salomatic-used-langtrace-to-build-a-reliable-medical-report-generation-system) | +| **[Truelaw](https://www.youtube.com/watch?v=O0F3RAWZNfM)** | How Truelaw builds bespoke LLM pipelines for law firms using DSPy. [Podcast](https://www.youtube.com/watch?v=O0F3RAWZNfM) | +| **[Moody's](https://www.moodys.com/)** | Leveraging DSPy to optimize RAG systems, LLM-as-a-Judge, and agentic systems for financial workflows. | +| **[Normal Computing](https://www.normalcomputing.com/)** | Translate specs from chip companies from English to intermediate formal languages | +| **[Procure.FYI](https://www.procure.fyi/)** | Process messy, publicly available technology spending and pricing data via DSPy. | +| **[RadiantLogic](https://www.radiantlogic.com/)** | AI Data Assistant. DSPy is used for the agent that routes the query, the context extraction module, the text-to-sql conversion engine, and the table summarization module. | +| **[Hyperlint](https://hyperlint.com)** | Uses DSPy to generate technical documentation. DSPy helps to fetch relevant information and synthesize that into tutorials. | +| **[Starops](https://staropshq.com/) & [Saya](https://heysaya.ai/)** | Building research documents given a user's corpus. Generate prompts to create more articles from example articles. | +| **[Tessel AI](https://tesselai.com/)** | Enhancing human-machine interaction with data use cases. | +| **[Dicer.ai](https://dicer.ai/)** | Uses DSPy for marketing AI to get the most from their paid ads. | +| **[Howie](https://howie.ai)** | Using DSPy to automate meeting scheduling through email. | +| **[Isoform.ai](https://isoform.ai)** | Building custom integrations using DSPy. | +| **[Trampoline AI](https://trampoline.ai)** | Uses DSPy to power their data-augmentation and LM pipelines. | WIP. This list mainly includes companies that have public posts or have OKed being included for specific products so far. + ## A Few Papers Using DSPy -| **Name** | **Description** | -| ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | -| **[STORM](https://arxiv.org/abs/2402.14207)** | Writing Wikipedia-like Articles From Scratch. | -| **[PATH](https://arxiv.org/abs/2406.11706)** | Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels | -| **[WangLab @ MEDIQA](https://arxiv.org/abs/2404.14544)** | UofT's winning system at MEDIQA, outperforms the next best system by 20 points | -| **[UMD's Suicide Detection System](https://arxiv.org/abs/2406.06608)** | Outperforms 20-hour expert human prompt engineering by 40% | -| **[IReRa](https://arxiv.org/abs/2401.12178)** | Infer-Retrieve-Rank: Extreme Classification with > 10,000 Labels | -| **[Unreasonably Effective Eccentric Prompts](https://arxiv.org/abs/2402.10949v2)** | General Prompt Optimization | -| **[Palimpzest](https://arxiv.org/abs/2405.14696)** | A Declarative System for Optimizing AI Workloads | -| **[AI Agents that Matter](https://arxiv.org/abs/2407.01502v1)** | Agent Efficiency Optimization | -| **[EDEN](https://arxiv.org/abs/2406.17982v1)** | Empathetic Dialogues for English Learning: Uses adaptive empathetic feedback to improve student grit | -| **[ECG-Chat](https://arxiv.org/pdf/2408.08849)** | Uses DSPy with GraphRAG for medical report generation | -| **[DSPy Assertions](https://arxiv.org/abs/2312.13382)** | Various applications of imposing hard and soft constraints on LM outputs | -| **[DSPy Guardrails](https://boxiyu.github.io/assets/pdf/DSPy_Guardrails.pdf)** | Reduce the attack success rate of CodeAttack, decreasing from 75% to 5% | -| **[Co-STORM](https://arxiv.org/pdf/2408.15232)** | Collaborative STORM: Generate Wikipedia-like articles through collaborative discourse among users and multiple LM agents | +| **Name** | **Description** | +|---|---| +| **[STORM](https://arxiv.org/abs/2402.14207)** | Writing Wikipedia-like Articles From Scratch. | +| **[PATH](https://arxiv.org/abs/2406.11706)** | Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels | +| **[WangLab @ MEDIQA](https://arxiv.org/abs/2404.14544)** | UofT's winning system at MEDIQA, outperforms the next best system by 20 points | +| **[UMD's Suicide Detection System](https://arxiv.org/abs/2406.06608)** | Outperforms 20-hour expert human prompt engineering by 40% | +| **[IReRa](https://arxiv.org/abs/2401.12178)** | Infer-Retrieve-Rank: Extreme Classification with > 10,000 Labels | +| **[Unreasonably Effective Eccentric Prompts](https://arxiv.org/abs/2402.10949v2)** | General Prompt Optimization | +| **[Palimpzest](https://arxiv.org/abs/2405.14696)** | A Declarative System for Optimizing AI Workloads | +| **[AI Agents that Matter](https://arxiv.org/abs/2407.01502v1)** | Agent Efficiency Optimization | +| **[EDEN](https://arxiv.org/abs/2406.17982v1)** | Empathetic Dialogues for English Learning: Uses adaptive empathetic feedback to improve student grit | +| **[ECG-Chat](https://arxiv.org/pdf/2408.08849)** | Uses DSPy with GraphRAG for medical report generation | +| **[DSPy Assertions](https://arxiv.org/abs/2312.13382)** | Various applications of imposing hard and soft constraints on LM outputs | +| **[DSPy Guardrails](https://boxiyu.github.io/assets/pdf/DSPy_Guardrails.pdf)** | Reduce the attack success rate of CodeAttack, decreasing from 75% to 5% | +| **[Co-STORM](https://arxiv.org/pdf/2408.15232)** | Collaborative STORM: Generate Wikipedia-like articles through collaborative discourse among users and multiple LM agents | ## A Few Repositories (or other OSS examples) using DSPy -| **Name** | **Description/Link** | -| --------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | -| **Stanford CS 224U Homework** | [Github](https://github.com/cgpotts/cs224u/blob/main/hw_openqa.ipynb) | -| **STORM Report Generation (10,000 GitHub stars)** | [Github](https://github.com/stanford-oval/storm) | -| **DSPy Redteaming** | [Github](https://github.com/haizelabs/dspy-redteam) | -| **DSPy Theory of Mind** | [Github](https://github.com/plastic-labs/dspy-opentom) | -| **Indic cross-lingual Natural Language Inference** | [Github](https://github.com/saifulhaq95/DSPy-Indic/blob/main/indicxlni.ipynb) | -| **Optimizing LM for Text2SQL using DSPy** | [Github](https://github.com/jjovalle99/DSPy-Text2SQL) | -| **DSPy PII Masking Demo by Eric Ness** | [Colab](https://colab.research.google.com/drive/1KZR1sGTp_RLWUJPAiK1FKPKI-Qn9neUm?usp=sharing) | -| **DSPy on BIG-Bench Hard Example** | [Github](https://drchrislevy.github.io/posts/dspy/dspy.html) | -| **Building a chess playing agent using DSPy** | [Github](https://medium.com/thoughts-on-machine-learning/building-a-chess-playing-agent-using-dspy-9b87c868f71e) | -| **Ittia Research Fact Checking** | [Github](https://github.com/ittia-research/check) | -| **Strategic Debate via Tree-of-Thought** | [Github](https://github.com/zbambergerNLP/strategic-debate-tot) | -| **Sanskrit to English Translation App** | [Github](https://github.com/ganarajpr/sanskrit-translator-dspy) | -| **DSPy for extracting features from PDFs on arXiv** | [Github](https://github.com/S1M0N38/dspy-arxiv) | -| **DSPygen: DSPy in Ruby on Rails** | [Github](https://github.com/seanchatmangpt/dspygen) | -| **DSPy Inspector** | [Github](https://github.com/Neoxelox/dspy-inspector) | -| **DSPy with FastAPI** | [Github](https://github.com/diicellman/dspy-rag-fastapi) | -| **DSPy for Indian Languages** | [Github](https://github.com/saifulhaq95/DSPy-Indic) | -| **Hurricane: Blog Posts with Generative Feedback Loops!** | [Github](https://github.com/weaviate-tutorials/Hurricane) | -| **RAG example using DSPy, Gradio, FastAPI, and Ollama** | [Github](https://github.com/diicellman/dspy-gradio-rag) | -| **Synthetic Data Generation** | [Github](https://colab.research.google.com/drive/1CweVOu0qhTC0yOfW5QkLDRIKuAuWJKEr?usp=sharing) | -| **Self Discover** | [Github](https://colab.research.google.com/drive/1GkAQKmw1XQgg5UNzzy8OncRe79V6pADB?usp=sharing) | +| **Name** | **Description/Link** | +|---|---| +| **Stanford CS 224U Homework** | [Github](https://github.com/cgpotts/cs224u/blob/main/hw_openqa.ipynb) | +| **STORM Report Generation (10,000 GitHub stars)** | [Github](https://github.com/stanford-oval/storm) | +| **DSPy Redteaming** | [Github](https://github.com/haizelabs/dspy-redteam) | +| **DSPy Theory of Mind** | [Github](https://github.com/plastic-labs/dspy-opentom) | +| **Indic cross-lingual Natural Language Inference** | [Github](https://github.com/saifulhaq95/DSPy-Indic/blob/main/indicxlni.ipynb) | +| **Optimizing LM for Text2SQL using DSPy** | [Github](https://github.com/jjovalle99/DSPy-Text2SQL) | +| **DSPy PII Masking Demo by Eric Ness** | [Colab](https://colab.research.google.com/drive/1KZR1sGTp_RLWUJPAiK1FKPKI-Qn9neUm?usp=sharing) | +| **DSPy on BIG-Bench Hard Example** | [Github](https://drchrislevy.github.io/posts/dspy/dspy.html) | +| **Building a chess playing agent using DSPy** | [Github](https://medium.com/thoughts-on-machine-learning/building-a-chess-playing-agent-using-dspy-9b87c868f71e) | +| **Ittia Research Fact Checking** | [Github](https://github.com/ittia-research/check) | +| **Strategic Debate via Tree-of-Thought** | [Github](https://github.com/zbambergerNLP/strategic-debate-tot) | +| **Sanskrit to English Translation App**| [Github](https://github.com/ganarajpr/sanskrit-translator-dspy) | +| **DSPy for extracting features from PDFs on arXiv**| [Github](https://github.com/S1M0N38/dspy-arxiv) | +| **DSPygen: DSPy in Ruby on Rails**| [Github](https://github.com/seanchatmangpt/dspygen) | +| **DSPy Inspector**| [Github](https://github.com/Neoxelox/dspy-inspector) | +| **DSPy with FastAPI**| [Github](https://github.com/diicellman/dspy-rag-fastapi) | +| **DSPy for Indian Languages**| [Github](https://github.com/saifulhaq95/DSPy-Indic) | +| **Hurricane: Blog Posts with Generative Feedback Loops!**| [Github](https://github.com/weaviate-tutorials/Hurricane) | +| **RAG example using DSPy, Gradio, FastAPI, and Ollama**| [Github](https://github.com/diicellman/dspy-gradio-rag) | +| **Synthetic Data Generation**| [Github](https://colab.research.google.com/drive/1CweVOu0qhTC0yOfW5QkLDRIKuAuWJKEr?usp=sharing) | +| **Self Discover**| [Github](https://colab.research.google.com/drive/1GkAQKmw1XQgg5UNzzy8OncRe79V6pADB?usp=sharing) | TODO: This list in particular is highly incomplete. There are a couple dozen other good ones. ## A Few Providers, Integrations, and related Blog Releases -| **Name** | **Link** | -| -------------------------- | -------------------------------------------------------------------------------------------------------- | -| **Databricks** | [Link](https://www.databricks.com/blog/dspy-databricks) | -| **Zenbase** | [Link](https://zenbase.ai/) | -| **LangWatch** | [Link](https://langwatch.ai/blog/introducing-dspy-visualizer) | -| **Gradient** | [Link](https://gradient.ai/blog/achieving-gpt-4-level-performance-at-lower-cost-using-dspy) | -| **Snowflake** | [Link](https://medium.com/snowflake/dspy-snowflake-140d6d947d73) | -| **Langchain** | [Link](https://python.langchain.com/v0.2/docs/integrations/providers/dspy/) | -| **Weaviate** | [Link](https://weaviate.io/blog/dspy-optimizers) | -| **Qdrant** | [Link](https://qdrant.tech/documentation/frameworks/dspy/) | -| **Weights & Biases Weave** | [Link](https://weave-docs.wandb.ai/guides/integrations/dspy/) | -| **Milvus** | [Link](https://milvus.io/docs/integrate_with_dspy.md) | -| **Neo4j** | [Link](https://neo4j.com/labs/genai-ecosystem/dspy/) | -| **Lightning AI** | [Link](https://lightning.ai/lightning-ai/studios/dspy-programming-with-foundation-models) | -| **Haystack** | [Link](https://towardsdatascience.com/automating-prompt-engineering-with-dspy-and-haystack-926a637a3f43) | -| **Arize** | [Link](https://docs.arize.com/phoenix/tracing/integrations-tracing/dspy) | -| **LlamaIndex** | [Link](https://github.com/stanfordnlp/dspy/blob/main/examples/llamaindex/dspy_llamaindex_rag.ipynb) | -| **Langtrace** | [Link](https://docs.langtrace.ai/supported-integrations/llm-frameworks/dspy) | -| **Langfuse** | [Link](https://langfuse.com/docs/integrations/dspy) | +| **Name** | **Link** | +|---|---| +| **Databricks** | [Link](https://www.databricks.com/blog/dspy-databricks) | +| **Zenbase** | [Link](https://zenbase.ai/) | +| **LangWatch** | [Link](https://langwatch.ai/blog/introducing-dspy-visualizer) | +| **Gradient** | [Link](https://gradient.ai/blog/achieving-gpt-4-level-performance-at-lower-cost-using-dspy) | +| **Snowflake** | [Link](https://medium.com/snowflake/dspy-snowflake-140d6d947d73) | +| **Langchain** | [Link](https://python.langchain.com/v0.2/docs/integrations/providers/dspy/) | +| **Weaviate** | [Link](https://weaviate.io/blog/dspy-optimizers) | +| **Qdrant** | [Link](https://qdrant.tech/documentation/frameworks/dspy/) | +| **Weights & Biases Weave** | [Link](https://weave-docs.wandb.ai/guides/integrations/dspy/) | +| **Milvus** | [Link](https://milvus.io/docs/integrate_with_dspy.md) | +| **Neo4j** | [Link](https://neo4j.com/labs/genai-ecosystem/dspy/) | +| **Lightning AI** | [Link](https://lightning.ai/lightning-ai/studios/dspy-programming-with-foundation-models) | +| **Haystack** | [Link](https://towardsdatascience.com/automating-prompt-engineering-with-dspy-and-haystack-926a637a3f43) | +| **Arize** | [Link](https://docs.arize.com/phoenix/tracing/integrations-tracing/dspy) | +| **LlamaIndex** | [Link](https://github.com/stanfordnlp/dspy/blob/main/examples/llamaindex/dspy_llamaindex_rag.ipynb) | +| **Langtrace** | [Link](https://docs.langtrace.ai/supported-integrations/llm-frameworks/dspy) | +| **Langfuse** | [Link](https://langfuse.com/docs/integrations/dspy) | ## A Few Blogs & Videos on using DSPy -| **Name** | **Link** | -| ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | -| **Blog Posts** | | -| **Why I bet on DSPy** | [Blog](https://blog.isaacbmiller.com/posts/dspy) | -| **Not Your Average Prompt Engineering** | [Blog](https://jina.ai/news/dspy-not-your-average-prompt-engineering/) | -| **Why I'm excited about DSPy** | [Blog](https://substack.stephen.so/p/why-im-excited-about-dspy) | -| **Achieving GPT-4 Performance at Lower Cost** | [Link](https://gradient.ai/blog/achieving-gpt-4-level-performance-at-lower-cost-using-dspy) | -| **Prompt engineering is a task best left to AI models** | [Link](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) | +| **Name** | **Link** | +|---|---| +| **Blog Posts** | | +| **Why I bet on DSPy** | [Blog](https://blog.isaacbmiller.com/posts/dspy) | +| **Not Your Average Prompt Engineering** | [Blog](https://jina.ai/news/dspy-not-your-average-prompt-engineering/) | +| **Why I'm excited about DSPy** | [Blog](https://substack.stephen.so/p/why-im-excited-about-dspy) | +| **Achieving GPT-4 Performance at Lower Cost** | [Link](https://gradient.ai/blog/achieving-gpt-4-level-performance-at-lower-cost-using-dspy) | +| **Prompt engineering is a task best left to AI models** | [Link](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) | | **What makes DSPy a valuable framework for developing complex language model pipelines?** | [Link](https://medium.com/@sujathamudadla1213/what-makes-dspy-a-valuable-framework-for-developing-complex-language-model-pipelines-edfa5b4bcf9b) | -| **DSPy: A new framework to program your foundation models just by prompting** | [Link](https://www.linkedin.com/pulse/dspy-new-framework-program-your-foundation-models-just-prompting-lli4c/) | -| **Intro to DSPy: Goodbye Prompting, Hello Programming** | [Link](https://medium.com/towards-data-science/intro-to-dspy-goodbye-prompting-hello-programming-4ca1c6ce3eb9) | -| **DSPyGen: Revolutionizing AI** | [Link](https://www.linkedin.com/pulse/launch-alert-dspygen-20242252-revolutionizing-ai-sean-chatman--g9f1c/) | -| **Building an AI Assistant with DSPy** | [Link](https://www.linkedin.com/pulse/building-ai-assistant-dspy-valliappa-lakshmanan-vgnsc/) | -| **Videos** | | -| **DSPy Explained! (60K views)** | [Link](https://www.youtube.com/watch?v=41EfOY0Ldkc) | -| **DSPy Intro from Sephora (25K views)** | [Link](https://www.youtube.com/watch?v=D2HurSldDkE) | -| **Structured Outputs with DSPy** | [Link](https://www.youtube.com/watch?v=tVw3CwrN5-8) | -| **DSPy and ColBERT - Weaviate Podcast** | [Link](https://www.youtube.com/watch?v=CDung1LnLbY) | -| **SBTB23 DSPy** | [Link](https://www.youtube.com/watch?v=Dt3H2ninoeY) | -| **Optimization with DSPy and LangChain** | [Link](https://www.youtube.com/watch?v=4EXOmWeqXRc) | -| **Automated Prompt Engineering + Visualization** | [Link](https://www.youtube.com/watch?v=eAZ2LtJ6D5k) | -| **Transforming LM Calls into Pipelines** | [Link](https://www.youtube.com/watch?v=NoaDWKHdkHg) | -| **NeurIPS Hacker Cup: DSPy for Code Gen** | [Link](https://www.youtube.com/watch?v=gpe-rtJN8z8) | -| **MIPRO and DSPy - Weaviate Podcast** | [Link](https://www.youtube.com/watch?v=skMH3DOV_UQ) | -| **Getting Started with RAG in DSPy** | [Link](https://www.youtube.com/watch?v=CEuUG4Umfxs) | -| **Adding Depth to DSPy Programs** | [Link](https://www.youtube.com/watch?v=0c7Ksd6BG88) | -| **Programming Foundation Models with DSPy** | [Link](https://www.youtube.com/watch?v=Y94tw4eDHW0) | -| **DSPy End-to-End: SF Meetup** | [Link](https://www.youtube.com/watch?v=Y81DoFmt-2U) | -| **Monitoring & Tracing DSPy with Langtrace** | [Link](https://langtrace.ai/blog/announcing-dspy-support-in-langtrace) | -| **Teaching chat models to solve chess puzzles using DSPy + Finetuning** | [Link](https://raw.sh/posts/chess_puzzles) | +| **DSPy: A new framework to program your foundation models just by prompting** | [Link](https://www.linkedin.com/pulse/dspy-new-framework-program-your-foundation-models-just-prompting-lli4c/) | +| **Intro to DSPy: Goodbye Prompting, Hello Programming** | [Link](https://medium.com/towards-data-science/intro-to-dspy-goodbye-prompting-hello-programming-4ca1c6ce3eb9) | +| **DSPyGen: Revolutionizing AI** | [Link](https://www.linkedin.com/pulse/launch-alert-dspygen-20242252-revolutionizing-ai-sean-chatman--g9f1c/) | +| **Building an AI Assistant with DSPy** | [Link](https://www.linkedin.com/pulse/building-ai-assistant-dspy-valliappa-lakshmanan-vgnsc/) | +| **Videos** | | +| **DSPy Explained! (60K views)** | [Link](https://www.youtube.com/watch?v=41EfOY0Ldkc) | +| **DSPy Intro from Sephora (25K views)** | [Link](https://www.youtube.com/watch?v=D2HurSldDkE) | +| **Structured Outputs with DSPy** | [Link](https://www.youtube.com/watch?v=tVw3CwrN5-8) | +| **DSPy and ColBERT - Weaviate Podcast** | [Link](https://www.youtube.com/watch?v=CDung1LnLbY) | +| **SBTB23 DSPy** | [Link](https://www.youtube.com/watch?v=Dt3H2ninoeY) | +| **Optimization with DSPy and LangChain** | [Link](https://www.youtube.com/watch?v=4EXOmWeqXRc) | +| **Automated Prompt Engineering + Visualization** | [Link](https://www.youtube.com/watch?v=eAZ2LtJ6D5k) | +| **Transforming LM Calls into Pipelines** | [Link](https://www.youtube.com/watch?v=NoaDWKHdkHg) | +| **NeurIPS Hacker Cup: DSPy for Code Gen** | [Link](https://www.youtube.com/watch?v=gpe-rtJN8z8) | +| **MIPRO and DSPy - Weaviate Podcast** | [Link](https://www.youtube.com/watch?v=skMH3DOV_UQ) | +| **Getting Started with RAG in DSPy** | [Link](https://www.youtube.com/watch?v=CEuUG4Umfxs) | +| **Adding Depth to DSPy Programs** | [Link](https://www.youtube.com/watch?v=0c7Ksd6BG88) | +| **Programming Foundation Models with DSPy** | [Link](https://www.youtube.com/watch?v=Y94tw4eDHW0) | +| **DSPy End-to-End: SF Meetup** | [Link](https://www.youtube.com/watch?v=Y81DoFmt-2U) | +| **Monitoring & Tracing DSPy with Langtrace** | [Link](https://langtrace.ai/blog/announcing-dspy-support-in-langtrace) | +| **Teaching chat models to solve chess puzzles using DSPy + Finetuning** | [Link](https://raw.sh/posts/chess_puzzles) | TODO: This list in particular is highly incomplete. There are dozens of other good ones. To allow space, divide into opintionated blogs / podcasts / interviews vs. tutorials & talks.