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Covering everything you need to know in order to build AI solutions faster.
Traditional AI chatbots are static. They know what they knew on day one and never improve. This article demonstrates how to build a customer support
AI agents promise efficiency, automation, and growth—but they’re not a magic fix. Many businesses jump in headfirst, only to find these “intelligent” systems creating more problems than they solve. From misaligned objectives to hidden operational risks, the pitfalls are real—and costly. In this article, we explore why AI agents can inadvertently harm your business and, more importantly, how you can implement them responsibly to unlock their true potential.
If you’re experimenting with agents, one thing I’ve learned is that finetuning isn’t just about improving accuracy — it’s about giving your agent a stable “personality” and predictable behaviour. RAG and prompts can only take you so far before the model starts drifting, especially in multi-step workflows or long-running automations.
The development of goal-oriented AI agents is driven by the need to bridge the gap between simple data retrieval and advanced reasoning capabilities. Traditional AI
Discover domain-specific LLMs, fine-tuning strategies, data annotation best practices, real-world use cases, challenges, and ethical considerations.
You’ve probably seen the hype around AI agents. They’re supposed to revolutionize how we work, answering questions, querying databases, writing reports. But here’s the truth: most AI agents fail spectacularly when deployed in real business environments.
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