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Integrating AI tools into your workflow

#Integrating #tools #workflow

Generative AI (GenAI) can write impressive essays, produce original images, and perform useful, time-saving analysis of huge datasets. With new capabilities appearing seemingly every day, everyone is wondering what they could accomplish with GenAI.

But for AI tools to be useful to your team, they have to fit into your existing workflows. When AI tools aren’t properly integrated, they interrupt rather than enhance the flow state. Constant context-switching is costly, and AI tools should make such interruptions less, rather than more, frequent.

In this article, we’ll cover some best practices for integrating AI tools into your technical workflows, from identifying the right tools and training your team to monitoring and optimizing the tools’ performance over time.

It’s important to clarify your goals in using new AI tools. It’s tempting, but trying every product you can get your hands on creates too much noise and introduces unnecessary risk. Think about the needs of your team and the challenges your organization is facing. Assess your criteria for speed, size, security, and privacy. Do you want a chatbot that can make recommendations based on a set of criteria, or a search engine that will respond with relevant data when your team has complex questions about an upcoming project?

Not every organization has the same needs, of course, but in general terms, a GenAI system integration involves connecting the model to the relevant data or documentation, deciding what information you’ll use to train the model, and determining what data it will have access to when it’s generating answers to user queries.

GenAI systems hinge on a technology called vector embedding. To integrate AI tools into your business, you’ll need to use an embedding model and store the results in a vector database. There are lots of embedding models to choose from, some from private companies and others open-source. You’ll probably want to start with free, open-source options if you’re prototyping something internally. You can always scale up to enterprise-grade solutions once you settle on the ideal approach.

For your database, there are purpose-built options from companies that are entirely focused on vector databases, as well as options from providers you might already work with. If your company already has a large account with a firm like MongoDB, for example, it might make sense to utilize their recently released vector database offering, as it can sit alongside and integrate with your existing database. If you’re starting from scratch, you might want to look at providers like Pinecone and Weaviate, which are focused on this specific niche and bring a wealth of knowledge and support to customers trying to create their first LLM-powered apps.

Want to rely on an external partner to handle the heavy lifting? Companies like OpenAI have done the work of creating a foundational model with its own embedding and vector database. You can use their Completions API to send your user queries to their system and feed their responses back. Just remember that there are tradeoffs around speed, security, privacy, and cost to consider when relying entirely on a third party.

In order for your team to take full advantage of whatever GenAI tools you end up introducing into your workflow, proper training is critical. That means training the AI itself on high-quality data that is complete, correct, and up-to-date, so you get accurate, reliable answers.

It also means training your human team to ask the right questions and refine those queries, providing them with comprehensive documentation, and capturing and preserving their knowledge for the benefit of future users. This is where a healthy knowledge management (KM) strategy becomes critical.

Gradually introducing GenAI tools into your technical teams’ daily workflows will be more effective in terms of adoption than suddenly forcing everyone to use a bunch of new tools instead of the ones they’re familiar with. Similarly, starting with small-scale or low-stakes tasks is a good way to demonstrate proof of concept and get people on board.

Allowing users to interact with the AI via chat or email can lower the barrier to adoption by incorporating GenAI into a tool users already have at their fingertips. At Stack Overflow, for example, we’re planning a Slack integration that will allow employees to engage with the AI without even opening a new window, much less shifting to an unfamiliar platform.

Another great way to integrate AI is through pre-trained text embeddings like the Universal Sentence Decoder, which “encode text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.” It takes just a few lines of Python to package your data for use by a GenAI-powered chatbot. Or go even further with this tutorial for prototyping a language-powered app using Google Sheets and an add-on called Semantic Reactor.

GenAI doesn’t offer set-it-and-forget-it tools, at least not yet. Assessing and optimizing the effectiveness of AI tools requires continuous monitoring, feedback, and adjustment based on performance metrics.

One thing to consider is that once you’ve spent time and money training an AI model, getting your embedding into a vector database, and deploying it, it can be difficult to update that database without re-running the training cycle. We touched on this during a conversation with Louis Brandy, VP of Engineering at Rockset, for our podcast.

Most technologists are eager to understand how to integrate AI tools into their workflows, but there are some best practices to bear in mind going in. Knowledge management tools can ensure that the data your AI is trained on is complete, accurate, and up-to-date: in fact, the right knowledge management strategy is foundational to success with AI tools.

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