Category: AI & Machine Learning - Page 2

13 February 2026 How User Feedback Loops Fix AI Hallucinations in Real-World Applications
How User Feedback Loops Fix AI Hallucinations in Real-World Applications

User feedback loops are essential for catching and correcting AI hallucinations in production. They rebuild trust, reduce errors by up to 60%, and are now required by regulations. Learn how to build one that actually works.

12 February 2026 Automated Architecture Lints: Enforcing Boundaries in Vibe-Coded Apps
Automated Architecture Lints: Enforcing Boundaries in Vibe-Coded Apps

Automated architecture lints enforce structural boundaries in AI-generated code, preventing chaos in vibe-coded apps. Without them, fast development turns into technical debt. Here's how they work, why they matter, and how to use them.

10 February 2026 Sales and Generative AI: How Battlecards, Call Summaries, and Objection Handling Are Changing Deals
Sales and Generative AI: How Battlecards, Call Summaries, and Objection Handling Are Changing Deals

Generative AI is transforming sales by turning static battlecards into real-time decision tools. See how call summaries and objection handling are getting smarter - and how your team can win more deals without working harder.

8 February 2026 How to Capture Project Style Guides in System Prompts for AI Consistency
How to Capture Project Style Guides in System Prompts for AI Consistency

Embedding project style guides into system prompts ensures AI outputs stay consistent, professional, and on-brand. Learn how top teams use structure, testing, and modular rules to control AI tone and format at scale.

7 February 2026 Retrieval Chunking Strategies That Improve LLM Grounding
Retrieval Chunking Strategies That Improve LLM Grounding

Effective retrieval chunking directly impacts LLM accuracy. Learn how sliding window, semantic, LLM-based, and chunking-free methods improve grounding-and which one to use for your use case.

6 February 2026 How Sci-LLMs Are Transforming Scientific Research: Key Insights and Practical Applications
How Sci-LLMs Are Transforming Scientific Research: Key Insights and Practical Applications

Sci-LLMs speed up literature reviews and hypothesis generation but require human oversight. This article explains their capabilities, real-world uses, and challenges researchers face today.

4 February 2026 Token Probability Calibration in LLMs: Improving Confidence Signals for Reliable AI
Token Probability Calibration in LLMs: Improving Confidence Signals for Reliable AI

Learn how token probability calibration ensures LLMs' confidence matches reality, why it's critical for healthcare and finance, and practical steps to implement it. Includes metrics, techniques, and industry trends.

3 February 2026 The Next Wave of Vibe Coding Tools: What's Missing Today
The Next Wave of Vibe Coding Tools: What's Missing Today

Vibe coding tools are fast and powerful-but they still can't design systems. Discover what's missing today in AI-powered development and what's coming in 2026 to bridge the gap between code generation and real architecture.

2 February 2026 Scenario Modeling for Generative AI Investments: Best, Base, and Worst Cases
Scenario Modeling for Generative AI Investments: Best, Base, and Worst Cases

Learn how generative AI transforms investment decisions by modeling best, base, and worst-case scenarios for AI investments. See real data on ROI, adoption trends, and how to avoid costly mistakes.

1 February 2026 SLAs and Support: What Enterprises Really Need from LLM Providers in 2026
SLAs and Support: What Enterprises Really Need from LLM Providers in 2026

Enterprises need more than fast AI-they need reliable, secure, and compliant LLM services. Learn what SLAs must include in 2026: uptime, latency, compliance, support, and hidden costs that could cost millions.

30 January 2026 Testing Strategies for Vibe-Coded Architectures: Unit, Contract, and E2E
Testing Strategies for Vibe-Coded Architectures: Unit, Contract, and E2E

Vibe coding accelerates development but introduces new testing risks. Learn how to use unit, contract, and end-to-end tests to catch AI-generated logic errors before they reach production.

29 January 2026 When Smaller, Heavily-Trained Large Language Models Beat Bigger Ones
When Smaller, Heavily-Trained Large Language Models Beat Bigger Ones

Smaller, heavily-trained language models are outperforming larger ones in speed, cost, and efficiency - especially for coding and developer tools. Discover why SLMs like Phi-2 and Gemma 2B are becoming the new standard.