Structured Data Querying: The Unbelievable Efficiency of Generative AI

Welcome to our latest blog post! Meet floresco.ai, a London-based company providing AI-powered customer engagement solutions. Our platforms, driven by advanced technology, facilitate seamless communication between businesses and customers via chat, voice, and email. We cater to diverse industries such as online shopping, finance, and healthcare, offering support for various queries, including order tracking and refunds.

Let's discuss the challenges faced by a chatbot development company serving a massive user base of 200 million worldwide. Tasked with creating a chatbot for a political figure, their goal is to address followers’ inquiries about socio-economic contributions. Managing extensive data stored in Excel sheets, including thousands of records related to government projects and schemes, the company faced the challenge of extracting relevant information and crafting natural responses. To overcome this obstacle, we suggested Generative AI. The integration of Generative AI significantly enhances the chatbot’s performance, leading to heightened user satisfaction.

Learn how Gen AI transformed data challenges into seamless user experiences for this chatbot company. Explore the GitHub link to install and try it yourself, or reach out if your company needs any assistance.

Business Challenges with Structured Data

In our blog, we’ll delve into the challenges encountered while working with structured and unstructured data. These challenges include:

1. Complexity in Query Writing:

   Users can ask various types of questions, making it impractical to pre-build all possible query combinations with structured data. This complexity adds difficulty to the task of writing effective database queries.

   
   

2. Data Synthesis:

   With structured data, information may exist in diverse formats and locations, making it challenging to consolidate and summarise in advance for all potential user inquiries. This diversity complicates the process of synthesising information for effective responses.

   
   

3. Time-Consuming Manual Effort:

   Manually summarising structured data from different perspectives is labour-intensive and cannot cover all potential use cases comprehensively. This manual effort consumes significant time and resources without guaranteeing comprehensive coverage.

   
   

4. Generating Personalised Answers:

   With structured data, users may phrase their queries in various forms, making it difficult to provide personalised responses tailored to each user’s specific context. This challenge adds complexity to generating responses that effectively address user inquiries.

Gen AI: Overview and Its Capabilities

Gen AI, a robust artificial intelligence platform, offers tools and models for tackling complex problems, including data analysis. Leveraging advanced machine learning techniques like natural language processing (NLP), it comprehends and generates text akin to human writing. Trained on extensive datasets, Gen AI ensures high accuracy in text analysis and generation. Beyond text, it excels in language translation, sentiment analysis, summarisation, and more. This technology revolutionises the study of structured and unstructured data, automating and enhancing the process significantly.

Enhancing Structured Data Analysis with Gen AI

Gen AI models streamline the conversion of natural language sentences into SQL queries, facilitating data analysis. This process simplifies the translation of human language into actionable commands for structured data. It generates SQL queries from posed questions and retrieves results from tables, presenting them in understandable natural language. This functionality benefits business analysts, data scientists, and non-technical users by providing access to database data without requiring SQL expertise.

How Our Solution Works with Gen AI

1. User question in natural language: The user asks a question or provides a query in a human-understandable way.

2. Instruction with user question: An instruction or prompt is initiated using the langchain framework for the SQL agent upon receiving the user’s query.

3. Define Action and Input: The system assesses the user’s query to select the best tool or method based on factors like requested information and tool capabilities.

4. Extracting Data from a Database: The system uses defined input to extract relevant data, employing the chosen tool.

5. Do observation and add to the agent prompt: The system analyses the retrieved data along with any additional context from the user’s query.

6. Return Thought: The system crafts a clear response based on the data.

7. Return a final answer to the user: The refined response is presented clearly and informatively to the user.

Tackling Structured Data Challenges for User Queries

1. Extracting Data for User Questions: We must extract relevant information from structured data by understanding and translating user queries into database queries.

2. Executing Generated Queries on the Database: Familiarising ourselves with the database schema ensures the queries align with the database structure, facilitating accurate data retrieval and analysis.

3. Interpreting Data Output for Effective Communication: Careful analysis of the response is necessary, followed by refining the query and communicating the final information to users in natural language.

4. Choosing the Optimal Gen AI Model: Selecting the best fit is crucial, with GPT-4 demonstrating superior performance despite higher costs.

5. Time Reduction in Querying using Gen AI: Our bot drastically minimises time spent on query processing compared to manual methods.

6. Enhancing User Satisfaction through Cost Optimisation and Caching: Implementing cost optimisation strategies such as caching improves overall system efficiency and user satisfaction.

7. Retrieving Data from Caching: Leveraging Cosine Similarity: Using cosine similarity and vector databases enhances user experience and system efficiency.

The Consequences of Not Implementing This Solution

In considering the ramifications of not implementing the solution at hand, it becomes apparent that several challenges may arise:

1. Time Consumption: Without leveraging this solution, tasks related to database management can become up to 40% more time-consuming.

2. Increased Effort in SQL Query Writing: The absence of this solution necessitates up to 35% more extensive effort in crafting SQL queries.

The Benefits of Implementing This Solution

1. Saving Development Time: This solution reduces development time by up to 50%.

2. Resource Savings: Resource requirements are reduced by approximately 30%.

3. Human Language Understanding: This solution simplifies interaction with databases, enabling intuitive data retrieval.

4. Handling Interconnected Tables: This solution enhances data accessibility and facilitates comprehensive analysis.

Limitations to Consider

While this solution offers significant advantages, it’s essential to acknowledge its limitations:

1. Response Generation Time: Users may experience delays in receiving responses.

2. Cost Considerations: Utilising this solution from OpenAI may incur costs.

Conclusion

So, what did we learn? With the right tools, like OpenAI, even tough challenges can be overcome. By using OpenAI, the chatbot company was able to save time and make their chatbot work better. Plus, they were able to reduce costs by 75%, since they no longer needed one person to analyse queries and create answers – OpenAI did it for them, and at a lower cost too!

If you’re facing the same issues and need our help, don’t hesitate to contact us for a free consultation.