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# Entry
It’s a myth that artificial intelligence (AI) projects take months to implement. The truth is that you can deploy an AI analyst who can answer convoluted business questions against your own Structured Query Language (SQL) database in minutes if you know how to successfully connect the right gigantic language model (LLM) to the data source.
In this article, I will describe how to implement an AI analyst Bag of wordsgroundbreaking AI data layer technology. You will learn practical, step-by-step processes that focus on SQL databases and the LLM. Along the way, we’ll discuss common implementation issues and ethical issues that every professional should be aware of.
# Understanding the bag of words
Bag of Words is an AI data layer platform that connects any LLM to almost any data source, including SQL databases PostgreSQL, MySQL, Snowflakeand more. It helps you build conversational AI analysts on your data with the following key features:
- It enables direct connection to existing data infrastructure
- Controls which tables and views the AI can access
- Improves data context with metadata from tools such as A vivid image Or db
- Safely manages user access and permissions
- It is designed for speedy, reliable and explainable insights
This approach simply means that users can “ask once, correct, and get results that can be explained,” all without massive engineering expenses.
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# Implementing an AI analyst
Many organizations struggle to unlock the full potential of their data despite having powerful tools. The problem is primarily integration, which is convoluted and there is no clear integration method. AI analysts powered by LLM transform raw data into insights using natural language queries, but accurately connecting these models to back-end data is critical.
The good news is that Bag of Words made it possible to connect SQL and LLM databases without the hassle of endless custom code. This lowers barriers and accelerates implementation from weeks or months to minutes, empowering both data teams and business users.
# Deploying an AI analyst with a bag of words
Follow these technical steps to quickly prepare an AI analyst to work in Docker.
// Step 1: Preparing the SQL database
- Ensure this Docker is installed on your computer and configured correctly before running the code below.
- Then run the following command:
docker run --pull always -d -p 3000:3000 bagofwords/bagofwords- If you are fresh, you need to register:
http://localhost:3000/users/sign-up.
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Follow the instructions to complete the onboarding process and configure your AI analyst.
- Make sure you have your SQL database connection credentials (host, port, username, password).
- Click Up-to-date Report. Then select any database. In this article, I will choose PostgreSQL.
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- Create a database and populate it. I recommend Supabase for a demonstration. You can exploit any one of your choice. Also make sure your database is accessible from the network where you will be deploying Bag of Words.
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- Find out which schemas, tables, and views contain the data you want the AI analyst to access.
- The next step is to give context to the analysis.
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Here you need to give the AI instructions on how to manage the data and you can connect to Tableau, dbt, Data formand yours AGENTS.md files in Git.
You can also start a conversation and with one click you will receive a ready-made answer containing all the necessary information.
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You can also configure and regenerate the report. Reporting on your data becomes autopilot.
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// Step 2: Test and refine your queries
- Interact with the AI analyst through the Bag of Words interface.
- Start with uncomplicated natural language queries such as “What were total sales last quarter?” or “Show top products by revenue.”
- Refine prompts and instructions based on initial results to improve accuracy and usefulness.
- Operate debugging tools to trace how LLM interprets the SQL and adjust the metadata as necessary.
// Step 3: Deploy and scale
- Integrate AI analyst with your business applications or reporting tools through APIs or user interface (UI) embedding.
- Monitor usage metrics and query performance to identify bottlenecks.
- As adoption grows, expand database access or model configurations iteratively.
# Challenges and solutions
Here are some obstacles you may encounter when implementing AI analysts (and how Bag of Words can support):
| Model | Train by | Val Akc | Gap | Risk of overfitting |
|---|---|---|---|---|
| Logistic regression | 91.2% | 92.1% | -0.9% | Low (negative gap) |
| Classification tree | 98.5% | 97.3% | 1.2% | Tiny |
| Neural network (5 nodes) | 90.7% | 89.8% | 0.9% | Tiny |
| Neural network (10 nodes) | 95.1% | 88.2% | 6.9% | Lofty – Throw it away |
| Neural network (14 nodes) | 99.3% | 85.4% | 13.9% | Very high – reject it |
# Summary
Onboarding an AI analyst in minutes by connecting any LLM to a SQL database is not only possible; this is becoming expected in today’s data-driven world. Bag of Words offers an accessible, pliant and secure way to quickly transform data into interactive, AI-powered insights. By following these steps, data scientists and business users alike can unlock fresh levels of productivity and transparency in decision-making.
If you’re struggling to successfully implement AI projects, now is the time to demystify the process, leverage fresh tools, and build an AI analyst with confidence.
Shittu Olumid is a software engineer and technical writer with a passion for using cutting-edge technology to create compelling narratives, with an eye for detail and a knack for simplifying convoluted concepts. You can also find Shittu on Twitter.