LLM Grounding: How to Keep AI Outputs Accurate and Reliable
Learn how to build reliable AI systems through LLM grounding. This technical guide covers implementation methods, real-world challenges, and practical solutions
More issues
Lifecycle of a Prompt
Learn how to master the prompt lifecycle for LLMs - from initial design to production monitoring. A practical guide for AI teams to build, test, and maintain effective prompts using Portkey's comprehensive toolset.
Challenges Agentic AI Companies Face in Enterprise Adoption
In this blog, we'll walk through the key hurdles teams face when bringing Agentic AI into enterprise environments
What is LLM Orchestration?
Learn how LLM orchestration manages model interactions, cuts costs, and boosts reliability in AI applications. A practical guide to managing language models with Portkey
Types of AI Guardrails and When to Use Them
A technical guide to implementing AI guardrails - covering input validation, output filtering, knowledge management, rate limiting, and compliance controls for production AI systems. Learn implementation patterns for safe, reliable AI deployment.
Why financial firms need granular governance for Gen AI
Learn how granular governance helps financial institutions scale AI systems securely, from maintaining compliance and protecting data to controlling costs and preventing misuse.
The State of AI FinOps 2025: Key Insights from FinOps Foundation's Latest Report
AI spending has doubled in enterprise environments, with a clear focus on establishing fundamentals before optimization. Dive into the latest FinOps Foundation report to understand how organizations are managing their AI infrastructure costs and what this means for your GenAI initiatives.
This is a summary blog focusing on AI trends
Open WebUI vs LibreChat: Choose the Right ChatGPT UI for Your Organization
Every organization wants to harness AI's transformative power. But the real challenge isn't accessing AI – it's doing so while maintaining complete control over your data. For healthcare providers handling patient records, financial institutions managing transactions, or companies navigating GDPR, this isn't just
Load balancing in multi-LLM setups: Techniques for optimal performance
Load balancing is crucial for teams running multi-LLM setups. Learn practical strategies for routing requests efficiently, from usage-based distribution to latency monitoring. Discover how to optimize costs, maintain performance, and handle failures gracefully across your LLM infrastructure.
Prompt engineering vs. fine-tuning: What’s better for your use case?
Discover the key differences between prompt engineering and model fine-tuning. Learn when to use each approach, how to measure effectiveness and the best tools for optimizing LLM performance.