llm
16 results
LLM Model Routing: Pick the Right Model for Every Task and Cut Costs 80%
Route LLM queries across nano, mid, and frontier tiers using LiteLLM and aicredits.in — same output quality, 80% lower API spend on mixed workloads.
Claude Sonnet 4 vs Opus 4: Which Model Should You Use?
Claude Opus 4 is more powerful but Sonnet 4 is faster and cheaper — here's exactly when the performance difference is worth the cost difference.
Gemini 2.5 Pro Prompting Guide: Get More Out of Google's Best Model
Gemini 2.5 Pro has a 1M token context window and strong reasoning — here's how to prompt it effectively for coding, research, and complex analysis tasks.
Gemini 2.5 Pro vs Claude Sonnet 4 for AI Agents: Which Is Better?
I built the same agent with both models. Here's what I found about tool use reliability, reasoning quality, and cost when running multi-step agentic tasks.
LLM Routing: How to Choose the Right Model for Each Task
Using the same model for everything is expensive and slow — here's how to route tasks to the right LLM based on complexity, cost, and latency requirements.
How to Write System Prompts for Grok (xAI) — What Works, What Doesn't
Grok has a distinct personality and handles system prompts differently from Claude or GPT-4o — here's what you need to know to get reliable results.
Context Engineering Is Eating Prompt Engineering
Why the 2025 shift from 'write a better prompt' to 'engineer the entire context window' changes how you build AI applications — and what to do about it.
Fine-Tuning vs. Prompting in 2026: When Each Actually Makes Sense
The calculus on fine-tuning has shifted significantly. Here's the updated decision framework for when prompting alone is enough and the specific cases where fine-tuning still wins.
How RAG Works: The Plain-English Guide to Retrieval Augmented Generation
RAG is the most widely used technique in production AI. Here's a clear, jargon-free explanation of how it works, why it matters, and when to use it.
What is Context Engineering? The Term Replacing 'Prompt Engineering' in 2025
Context engineering is the practice of designing everything that goes into an AI's context window — not just the prompt. Here's why it matters and how to get better at it.
What is an AI Agent?
Understand what separates an AI agent from a regular prompt. Learn how agents perceive, reason, act, and loop — and why this architecture unlocks a completely new class of AI applications.
Get Better Results from OpenClaw: Prompting Strategies
Practical strategies for improving OpenClaw's output quality — covering SOUL.md tuning, context management, model selection, memory hygiene, and common mistakes that degrade responses.
Deploy AI Apps on Hostinger VPS: No Timeouts
Serverless platforms choke on AI workloads — cold starts, 10-second timeouts, no streaming. Here's how to deploy a production AI app on Hostinger KVM VPS with proper SSE streaming, persistent LLM connections, and optional local model support.
LangGraph: Build Stateful AI Agents That Actually Work
LangGraph extends LangChain with graph-based agent architecture — nodes, edges, state, and cycles. Learn how to build reliable multi-step AI agents with real Python code examples.
