Essential interview questions designed for AI enthusiasts and professionals focusing on Large Language Models (LLMs).

The content systematically covers the foundational architectural elements of LLMs, explaining core concepts such as tokenization, the attention mechanism, and the function of the context window.

It differentiates advanced fine-tuning techniques like LoRA versus QLoRA and details sophisticated generation strategies, including beam search and temperature control.

Furthermore, the document addresses critical training mathematics, discussing topics like cross-entropy loss and the application of the chain rule in gradient computation. The resource concludes by reviewing modern applications like Retrieval-Augmented Generation (RAG) and the significant challenges LLMs face in real-world deployment.

Source: https://booking.ai/building-a-genai-agent-for-partner-guest-messaging-f54afb72e6cf

Author : Başak Tuğçe Eskili

Technical evaluation of Pipedream, an integration platform designed to bridge the gap between simple no-code tools and complex, raw serverless infrastructure like AWS Lambda. It details Pipedream's core serverless architecture, highlighting its support for multiple coding languages (Node.js, Python, Go, Bash) and its managed dependency resolution that simplifies developer workflow.

The document also explores advanced features crucial for enterprise readiness, such as built-in state management (Data Stores), robust flow control mechanisms for concurrency and throttling, and high-level compliance including SOC 2 Type 2 and HIPAA.

Furthermore, the evaluation covers the Connect product for embedding integrations into SaaS applications and analyzes the platform's cost-efficiency under a Compute Credit pricing model, suggesting significant savings compared to task-based competitors like Zapier.

Overview of Google Antigravity, a new autonomous, agentic development platform marking a significant shift from traditional AI assistant models in software engineering.

This platform is powered by the Gemini 3 model family, specifically leveraging the deep reasoning of Gemini 3 Deep Think and the whole-program awareness provided by a 1-million-token context window.

Antigravity integrates the Editor, Terminal, and Browser into a unified control plane, enabling AI agents to plan complex multi-step tasks, execute code, and visually verify outcomes, thereby raising the developer’s role from code author to agent architect.

The system emphasizes transparency through structured outputs called Artifacts and uses the Model Context Protocol (MCP) to connect agents to external resources like databases and issue trackers, creating a comprehensive and autonomous development workflow.

Analysis of LlamaIndex Document AI, positioning it as a next-generation platform that moves beyond traditional Optical Character Recognition (OCR) and Intelligent Document Processing (IDP).

It details the GenAI-native approach of LlamaParse, which uses Large Vision Models (LVMs) to semantically reconstruct complex documents into LLM-optimized formats like Markdown, solving layout issues that plague legacy systems like AWS Textract.

The report comprehensively explains the Agentic Document Workflows (ADW) framework, which uses event-driven orchestration to enable self-correcting, multi-step Reasoning-Augmented Generation (RAG) necessary for autonomous enterprise tasks.

Furthermore, the text examines the platform's architecture, including the LlamaCloud managed services, credit-based pricing models, security compliance (SOC 2, HIPAA), and includes case studies demonstrating significant workflow acceleration across regulated industries such as finance and healthcare.

Finally, it addresses ongoing challenges related to debugging non-deterministic systems and managing the complexity inherent in multi-agent architectures.

Technical and strategic analysis of how the company Pathwork revolutionized life insurance underwriting by implementing the LlamaIndex framework.

Historically challenged by manually processing unstructured medical records, Pathwork adopted the Retrieval-Augmented Generation (RAG) architecture, leveraging the specialized parser LlamaParse to handle complex, messy documents like handwritten notes and old scans.

This integration significantly scaled document processing to over 40,000 pages per week with a high pass-through rate, fundamentally shifting the process from slow, human-centric data entry to efficient, AI-centric automation.

The report also rigorously examines the critical issues of HIPAA compliance, architectural differences from hyperscaler competitors, and the future transition toward Agentic AI in the high-stakes, regulated insurance industry.

Overview of Sakana AI, a Tokyo-based research company challenging the conventional "Scaling Hypothesis" of artificial intelligence development.

Founded by key architects of the Transformer model, Sakana AI instead champions a "nature inspired intelligence" approach, emphasizing efficiency and collective systems over raw computational scale.

The core of their technology includes Evolutionary Model Merge and the AI Scientist agent, systems designed to automatically combine and optimize existing open-source models, drastically reducing energy costs.

Strategically, Sakana AI has positioned itself as the leader of Sovereign AI for Japan, forming crucial partnerships with entities like MUFG and the Ministry of Defense to modernize the nation's economy and ensure technological independence.

The analysis evaluates both the revolutionary potential and the inherent risks, such as agentic safety concerns and the challenge of competing against continuously scaling frontier models.

Analysis of Amazon’s Chronos-2, a Time Series Foundation Model (TSFM) that represents a paradigm shift from traditional, task-specific forecasting to a universal, pre-trained intelligence. It highlights that Chronos-2, built on a Transformer architecture and trained on massive synthetic data, overcomes the limitations of older univariate models—such as ARIMA—by natively incorporating external factors (covariates) through a novel Group Attention Mechanism. The source details how this capability allows the model to achieve state-of-the-art zero-shot performance on benchmarks and unlocks transformative applications across industries like retail, logistics, and technology.

Ultimately, the document positions Chronos-2 not merely as a new algorithm, but as a catalyst for a future where organizations leverage single, powerful foundation models instead of maintaining millions of individual forecasts, though it cautions that this requires significant maturity in data quality and organizational infrastructure.