Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)

Let’s stop guessing. Let’s search.

LLMs hallucinate. They don't know your private data.
In this episode, we dive into RAG, the architecture that changed how modern AI systems handle knowledge.

Instead of relying solely on parametric memory (weights), models can now retrieve external documents to produce factual answers.

We break down the 2020 seminal paper and explain:
🔹 Why Large Language Models hallucinate.
🔹 The "Retriever-Generator" architecture.
🔹 Why retrieval has become the backbone of Enterprise AI.

Clear intuition, real examples, and practical insight.

🎧 Listen now to master the tech behind ChatPDF and Search.

As we close the year, tell us what you think in the Q&A!

In this episode of AI Papers Explained, we explore Mamba: Linear-Time Sequence Modeling with Selective State Spaces, a 2023 paper by Albert Gu and Tri Dao that rethinks how AI handles long sequences.Unlike Transformers, which compare every token to every other, Mamba processes information linearly and selectively, remembering only what matters.

This marks a shift toward faster, more efficient architectures, a possible glimpse into the post-Transformer era.

When AI Learned to See:

In this fourth episode of AI Papers Explained, we explore Learning Transferable Visual Models From Natural Language Supervision — the 2021 OpenAI paper that introduced CLIP.After Transformers, BERT, and GPT-3 reshaped how AI understands language, CLIP marked the moment when AI began to see through words.By training on 400 million image-text pairs, CLIP learned to connect vision and language without manual labels.
This breakthrough opened the multimodal era-leading to DALL·E, GPT-4V, and Gemini.

Discover how contrastive learning turned internet captions into visual intelligence.