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Hack the Future: Supercharging AI with Retrieval-Augmented Generation

Learn how World Wide Technology is empowering early-career women in India to "Hack the future" through virtual workshops and hands-on labs. They're exploring Retrieval-Augmented Generation (RAG) to enhance AI systems and promote responsible, smarter AI development.

In this blog

  1. Why It Matters
  2. What We Learned
  3. Hands-On Labs: Learning by Doing
  4. Real Talk: Careers, Challenges, and Growth
  5.  Key Takeaways
  6. What's Next?

On a bright Saturday morning, nearly 70 early-in-career women from across India gathered virtually for a hands-on, high-energy workshop titled Hack the Future: Supercharging AI with Retrieval-Augmented Generation (RAG). Hosted by Ashley Schrage and Ina Poecher from World Wide Technology, the session was designed to demystify generative AI and empower participants to build smarter, context-aware systems using RAG.

 Why It Matters

The tech world is evolving rapidly, and generative AI is at the forefront. But as Ina reminded us, innovation thrives on a multitude of perspectives and inputs. Women in technology bring fresh perspectives, challenge bias, and drive progress and accuracy. This workshop wasn't just about learning—it was about building confidence, community, and capability.

 What We Learned

The session kicked off with a deep dive into the evolution of AI, from predictive models to generative systems and now agentic workflows. Ina broke down complex concepts with clarity, helping participants understand:

  • The limitations of traditional LLMs (like hallucinations and knowledge cutoffs)
  • How Retrieval-Augmented Generation (RAG) solves these problems by integrating external, real-time data
  • The architecture behind RAG pipelines—from document chunking and embedding to vector databases and query processing

 Hands-On Labs: Learning by Doing

Participants completed two guided labs, designed for all skill levels:

 1. RAG Walkthrough Lab

  • Loaded documents
  • Split them into meaningful chunks
  • Generated vector embeddings
  • Stored and retrieved relevant information using RAG

 2. RAG Programmatic Lab

  • Used Python and Jupyter Notebooks to build a working RAG pipeline
  • Compared LLM responses with and without RAG
  • Explored embedding models, chunking strategies, and vector search techniques

The labs were powered by open-source tools like LangChain, Llama 3.1, and LanceDB, showing that powerful AI can be built without expensive hardware.

 Real Talk: Careers, Challenges, and Growth

The final segment was a candid Q&A where Ashley and Ina shared their career journeys—from education and engineering to data science and sales enablement. They offered advice on:

  • Building impactful machine learning projects (like agentic workflows or computer vision for safety)
  • Learning Python and ML through free resources like W3Schools, NVIDIA, and Google
  • Finding internships and making the most of early career opportunities

  Key Takeaways

  • RAG is a game-changer: It makes AI smarter by giving it access to real-world, up-to-date data.
  • You don't need a GPU to get started: Many tools run on CPUs and are free to use.
  • Community matters: Learning alongside peers and mentors accelerates growth.
  • Your voice is needed in tech: Diversity drives better innovation.

 What's Next?

Participants were encouraged to share feedback and suggest future topics—from deeper dives into agentic AI to career development strategies. The energy was palpable, and the appetite for learning was strong.

Hack the Future wasn't just a workshop—it was a launchpad. For many, it was the first step into building real AI systems. For all, it was a reminder that the future of tech is brighter when we build it together.