Harness Engineering
Why production AI is 90% harness and 10% model. And what most pilots miss. Blake Aber · Predicate Ventures · 2026 The gap between an AI demo that land…
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Using LLM for Dialogue Management Tasks
Dialogue management is the logic layer that decides what a conversational system should do next. Traditional implementations rely on finite state mach…
Optimizing LLM Model Performance for Real-Time Applications
Real-time applications, from live coding assistants to conversational voice agents, require LLM latency measured in hundreds of milliseconds, not seco…
Using LLM for Dialogue Management
Dialogue management is the process of tracking conversational state and deciding what an agent should say or do next. Classical systems split this int…
Optimizing LLM Model Performance: Best Practices and Techniques
Production LLM workloads rarely fail because of model intelligence. They fail when latency spikes, context windows overflow, or inference costs scale …
Few-Shot Learning with LLM: A Deep Dive
Few-shot learning with large language models is one of the most practical ways to steer model behavior without updating weights. By embedding task-spe…
LLM Trends and Future Outlook
The conversation around large language models has shifted. The frontier is no longer defined solely by parameter counts or training compute, but by th…