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Case Study

How AskRoger Built an AI Personal Assistant for Any Content

Discover How AskRoger.ai used Deep Lake to Create an AI Assistant Streamlining Content Curation and Summarization for Multi-Modal, Cross-Platform Content

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Database for AI

Introducing AskRoger.ai: the AI-Powered Summary Assistant

In the era of information overload, AskRoger.ai provides an intelligent solution by monitoring and summarizing content across multiple channels, as well as answering questions over it. This AI-powered product effectively extracts valuable insights from various sources like articles, podcasts, newsletters, and videos. Its versatility extends to cross-platform availability with Messenger, Twitter, Slack, Email, and iOS support.

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How AskRoger.ai Works

The Way Forward: AskRoger's Future Plans in the AI Coding Assistant Domain

  1. AskRoger.ai is available as a chatbot assistant on several messaging platforms (Messenger, Twitter, and Slack). Requesting a summary is as easy as sending the link to the content (Youtube video, Spotify podcast, PDF, etc). Then you can ask questions in the conversation to learn more about said content.
  2. On Twitter, you can also request a content summary by mentioning @askrogerai in response to a tweet containing a link.
  3. AskRoger.ai can summarise newsletters as well. Forward any email to [email protected], and you will receive the summary in response.
  4. Finally, it is also possible to request a summary through an iOS shortcut which will add a convenient “summarize” button in the 'sharesheet' of every iOS app.

Meet the Interviewee

Jean-Charles Touzalin, the mastermind behind AskRoger.ai, is a seasoned entrepreneur and former Chief Technology Officer of Sport Heroes, with a solid background as a Software Engineer at Thales. He has always been an ardent follower of newsletters, subscribing to over 50 diverse sources, ranging from artificial intelligence to entrepreneurship.

However, as the content volume expanded, it became increasingly difficult for him to determine the articles of the most significant value. This complexity paved the way for the creation of AskRoger - an AI assistant designed to delve deep into the mine of information, tirelessly sifting through layers of content to discern the golden nuggets from common stones.

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The Challenges

Encountered Challenges: AskRoger.ai’s Quest for Efficient Content Summarization. Before turning to Deep Lake, AskRoger.ai grappled with challenges in handling extensive content:

  • 1

    Context Window Limitations of Large Language Models (LLMs)

    LLMs like GPT-4 or 3.5 from OpenAI can only process limited context, posing difficulties when dealing with sizeable content. This becomes particularly challenging when dealing with lengthy content such as hours-long podcasts or extensive white-papers.

  • 2

    Inaccurate Answers

    Discussing specific topical content and answering questions with plain GPT knowledge needed to be improved for the product since its main value proposition was the ability to chat with multiple documents and tailor the responses to those documents.

Solution

Activeloop's Deep Lake for Retrieval Augmented Generation. Deep Lake provided an optimal solution for AskRoger.ai's summarization capabilities. It allowed the AI to store the content as Open AI-generated embeddings (ada-002) for further analysis, enabling context-aware responsive features. In addition, it facilitated Retrieval Augmented Generation by conducting a similarity search for relevant content based on user queries.

When a user asks a question, the query is embedded as a vector and then used in a similarity search to fetch relevant content from Deep Lake. The relevant content is then fed to the LLM with the original question so that the AI can answer with the proper context. 

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Results

Scalable Database for Retrieval Augmented Generation over Multimodal AI data. Deep Lake brought substantial enhancements to AskRoger.ai's services

  • Scalable Database for AI
    With Deep Lake, AskRoger.ai Could Efficiently Embed, Query, and Retrieve Extensive, Multi-modal Content Expanding Beyond the Context Limitation of Large Language Models like GPT-4.
  • Retrieval Augmented Generation
    Deep Lake Enabled More Context-aware Responses to User Queries, Increasing Response Relevancy and User Satisfaction with Responses.
  • Multi-Modality
    The Implementation of Deep Lake Helped Store AskRoger.ai's Responses Which were Instrumental for Further Analysis and Fine-tuning of the Assistant.
“I chose Deep Lake among other vector databases due to its open-source nature, easy setup process, and developer-friendly features. It seamlessly integrates with AWS S3, which I found cost-effective and familiar. Moreover, it included a multi-modality feature that enabled us to store our content as embeddings for further analysis - a vital feature significantly improving our AI assistant's responsiveness.”

Jean-Charles Touzalin

AskRoger.ai Founder
Jean-Charles Touzalin

Future Plans for AskRoger.ai

The Way Forward: AskRoger's Future Plans in the AI Coding Assistant Domain. Moving forward, AskRoger.ai aims to refine its summarizing capabilities further and extend its reach across more platforms like Discord or browsers like Firefox and Chrome. Given its proven adaptability and efficiency in handling extensive content, Deep Lake is poised to help the AskRoger.ai team advance these plans.

AskRoger.ai is a valuable tool for individuals and professionals seeking smarter learning strategies in the face of information overload. By harnessing the power of cutting-edge AI technologies, it empowers users to summarize and extract insights from various content sources efficiently. With its easy onboarding and wide availability, AskRoger.ai paves the way for enhanced learning experiences and more informed decision-making in the digital age.

With Deep Lake's aid, AskRoger.ai transforms information overload into distilled, curated bits of helpful knowledge. Follow this link to start your journey with this AI-powered summary assistant today.

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