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Being a Co-founder, specifically in AI and Tech, would let me push innovation and solve real-world problems with a tool that I have been at the forefront of creating. I want to build, and I want to learn - this is the perfect mix.

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How Pro Founders ACTUALLY Build Startups

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I thoroughly enjoy creating and building AI and ML models, especially now with LLMs and agentic frameworks. This role would allow me to develop models that drive localized success, helping me understand deeply what a company needs and how I can address those needs.

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Here you'll find answers to "How do I....?" types of questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task. For conceptual explanations see the Conceptual guide. For end-to-end walkthroughs see Tutorials. For comprehensive descriptions of every class and function see the API Reference.

This highlights functionality that is core to using LangChain.

These are the core building blocks you can use when building applications.

Chat Models are newer forms of language models that take messages in and output a message. See supported integrations for details on getting started with chat models from a specific provider.

Messages are the input and output of chat models. They have some content and a role, which describes the source of the message.

Prompt Templates are responsible for formatting user input into a format that can be passed to a language model.

Example Selectors are responsible for selecting the correct few shot examples to pass to the prompt.

What LangChain calls LLMs are older forms of language models that take a string in and output a string.

Output Parsers are responsible for taking the output of an LLM and parsing into more structured format.

Document Loaders are responsible for loading documents from a variety of sources.

Text Splitters take a document and split into chunks that can be used for retrieval.

Embedding Models take a piece of text and create a numerical representation of it. See supported integrations for details on getting started with embedding models from a specific provider.

Vector stores are databases that can efficiently store and retrieve embeddings. See supported integrations for details on getting started with vector stores from a specific provider.

Retrievers are responsible for taking a query and returning relevant documents.

Indexing is the process of keeping your vectorstore in-sync with the underlying data source.

LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer here for a list of pre-buit tools.

Callbacks allow you to hook into the various stages of your LLM application's execution.

All of LangChain components can easily be extended to support your own versions.

These guides cover use-case specific details.

Retrieval Augmented Generation (RAG) is a way to connect LLMs to external sources of data. For a high-level tutorial on RAG, check out this guide.

Extraction is when you use LLMs to extract structured information from unstructured text. For a high level tutorial on extraction, check out this guide.

Chatbots involve using an LLM to have a conversation. For a high-level tutorial on building chatbots, check out this guide.

Query Analysis is the task of using an LLM to generate a query to send to a retriever. For a high-level tutorial on query analysis, check out this guide.

You can use LLMs to do question answering over tabular data. For a high-level tutorial, check out this guide.

You can use an LLM to do question answering over graph databases. For a high-level tutorial, check out this guide.

LLMs can summarize and otherwise distill desired information from text, including large volumes of text. For a high-level tutorial, check out this guide.

Should I use LCEL?

LangChain Expression Language is a way to create arbitrary custom chains. It is built on the Runnable protocol.

LCEL cheatsheet: For a quick overview of how to use the main LCEL primitives.

Migration guide: For migrating legacy chain abstractions to LCEL.

LangGraph is an extension of LangChain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.

LangGraph documentation is currently hosted on a separate site. You can peruse LangGraph how-to guides here.

LangSmith allows you to closely trace, monitor and evaluate your LLM application. It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build.

LangSmith documentation is hosted on a separate site. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below:

Evaluating performance is a vital part of building LLM-powered applications. LangSmith helps with every step of the process from creating a dataset to defining metrics to running evaluators.

To learn more, check out the LangSmith evaluation how-to guides.

Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues.

You can see general tracing-related how-tos in this section of the LangSmith docs.

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Here you'll find answers to "How do I....?" types of questions.

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I need to increase my skills in building infrastructure and knowing MLOps for optimized production environments for both of these roles.

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Understand MLOps, the practice of deploying and maintaining machine learning models in production reliably and efficiently, with Databricks.

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To work in a startup as a Co-founder and COO, I need to improve my leadership skills to align people and foster collaboration toward a common goal

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Being in an AI-focused research environment like DeepMind would be a dream. These companies are actively pushing the boundaries of AI and, in turn, the limits of human potential achievement.

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Artificial intelligence could be one of humanity's most useful inventions. We research and build safe artificial intelligence systems. We're committed to solving intelligence, to advance science and benefit humanity.

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Being in a startup environment fits my personality type really well. The simplicity of pushing innovation as hard as possible, where the direction isn’t necessarily set in stone, resonates greatly with me.

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Day in the Life of an AI Startup Founder | YCombinator

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Not essential, but it would demonstrate that I possess the necessary skills and specialization in my areas of interest to be a knowledgeable professional.

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The Online Master of Applied Data Science Program

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UNC-Chapel Hill Master of Applied Data Science | OnlineUNC-Chapel Hill Master of Applied Data Science | OnlineUNC-Chapel Hill Master of Applied Data Science | Online

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The use of cloud-based AI, such as in AWS, is becoming essential; obtaining a certification like this one from AWS would indicate that I am not only knowledgeable about AI but also capable of building and scaling it through cloud computing.

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AWS Certified AI Practitioner Certification | AWS Certification | AWS

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Earning AWS Certified AI Practitioner validates your understanding of artificial intelligence (AI), machine learning (ML), and generative AI concepts and use cases. Learn more about this certification and AWS Training and Certification resources that can help you prepare for your exam.

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Elad Gil is one of the world's top startup and AI investors. His success in helping companies like Airbnb, Google, and Twitter scale speaks for itself itself.

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Experience: Gil Capital · Location: San Francisco · 500+ connections on LinkedIn. View Elad Gil’s profile on LinkedIn, a professional community of 1 billion members.

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Demis has been a giant in AI from the beginning, having co-founded DeepMind. His philosophy to "solve intelligence" and then use that intelligence "to solve everything else" speaks volumes.

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Demis Hassabis - Founder & CEO - Isomorphic Labs | LinkedIn

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Co-Founder & CEO, Google DeepMind · Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Revolutionising drug discovery at Isomorphic Labs. Ultimately trying to understand the fundamental nature of reality. · Experience: Isomorphic Labs · Education: University College London, U. of London · Location: Greater London · 500+ connections on LinkedIn. View Demis Hassabis’ profile on LinkedIn, a professional community of 1 billion members.

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Measures AI is my startup. We aim to utilize AI to measure everything about a business, creating high-ROI KPIs by accessing data from its unstructured source - through emails, Slack, Teams, documentation, and any random application that companies use.

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One of my long-term stretch goals is to perform high-performance computational work for a library like NumPy or Pandas, which are widely used in Python programming. I have a passion for math and optimizing numerical methods through coding, so developing for an open-source library like this would be very fulfilling.

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Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source.

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There are several AI Startup / AI Founder communities in Boulder. The main one that I have attended is the Rocky Mountain AI Interest Group based in Boulder, where I have met some very interesting people, but I want to branch out and explore some more

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Rocky Mountain AI Interest Group RMAIIG | Meetup

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The Rocky Mountain AI Interest Group (RMAIIG) is a community of both non-technical and technical members who are passionate and enthusiastic about the recent advancements in generative AI tools. This group is focused on exploring and discussing the latest developments in AI, particularly tools like

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One of my goals for the next year is to speak at a conference (HR Tech or general Tech) about a topic related to AI; my company is working with a digital transformation consultant, Dana Sednek, to develop a joint talk.

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I swim for the CU Swim Team and am a citizen of Sweden. This year at nationals in April, in addition to breaking my school records in the 200 IM and 100 IM, I hope to qualify for Swedish Nationals, as I did two years ago, and compete this summer

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At some point in my life, I would love to have a sauna at home that I can use after a long day of work. This is unironically a top 5 goal for me.

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The benefits of sauna | Andrew Huberman and Lex Fridman

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One of my favorite quotes is, “On s'engage et puis on voit,” which translates more or less to, “Start something, then see what happens.” The quote serves as a reminder that you don’t need to know everything to begin something. Do, and see what unfolds.

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“He who has a why to live can bear almost any how." Although I do not identify as a nihilist (a philosophy that Nietzsche created), I strongly believe that having a 'why' is as important as having a home. If I don't genuinely believe in what I do or understand why I am doing something, then I cannot do it.

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How to know your life purpose in 5 minutes | Adam Leipzig | TEDxMalibu