![]() Sub-10% were looking for tools to monitor LLM outputs, cost, or performance and A/B test prompts.Adoption increased in the last few months. Some use it for prototyping, while others use it in production. 38% were interested in an LLM orchestration and application development framework like LangChain.Some use purpose-built vector databases (Pinecone, Weaviate, Chroma, Qdrant, Milvus, and many more), while others use pgvector or AWS offerings. Retrieving relevant context for a model to reason about helps increase the quality of results, reduce “hallucinations” (inaccuracies), and solve data freshness issues. 88% believe a retrieval mechanism, such as a vector database, would remain a key part of their stack.(Some companies are using multiple models). ![]() OpenAI’s GPT was the clear favorite in our sample at 91%, however Anthropic interest grew over the last quarter to 15%. 65% had applications in production, up from 50% two months ago, while the remainder are still experimenting.The new stack for these applications centers on language model APIs, retrieval, and orchestration, but open source usage is also growing. These are just a few examples and they’re only the beginning.Ģ. Others are reimagining entire workflows with an AI-first lens: visual art (Midjourney), marketing (Hubspot, Attentive, Drift, Jasper, Copy, Writer), sales (Gong), contact centers (Cresta), legal (Ironclad, Harvey), accounting (Pilot), productivity (Notion), data engineering (dbt), search (Glean, Neeva), grocery shopping (Instacart), consumer payments (Klarna), and travel planning (Airbnb). We’ve seen better chatbots for everything from customer support to employee support to consumer entertainment. We’ve seen magical auto-complete features for everything from code (Sourcegraph, Warp, Github) to data science (Hex). Nearly every company in the Sequoia network is building language models into their products. As many founders and builders are in the midst of figuring out their AI strategies themselves, we wanted to share our findings even as this space is rapidly evolving.ġ. We spoke with them two months ago and last week to capture the pace of change. To better understand the applications people are building and the stacks they are using to do so, we spoke with 33 companies across the Sequoia network, from seed stage startups to large public enterprises. ![]() The adoption of language model APIs is creating a new stack in its wake. More companies than ever before are bringing the power of natural language interaction to their products. ChatGPT unleashed a tidal wave of innovation with large language models (LLMs).
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