Prompt Compression: When to Use LLMLingua and Friends
Prompt compression reduces tokens 5-10x at modest quality cost. The 2026 patterns and where compression breaks.
Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Prompt compression reduces tokens 5-10x at modest quality cost. The 2026 patterns and where compression breaks.
Tool-calling reliability is mostly a prompt-engineering problem. The 2026 patterns that consistently improve function-call accuracy.
Ten concrete defensive patterns against direct and indirect prompt injection in production agents in 2026.
Provider lock-in is real but manageable with the right architecture. The 2026 mitigation patterns and what to abstract.
LLM provider pricing has matured beyond per-token list pricing. The 2026 commitments, reserved capacity, and spot tiers worth negotiating.
Provider SLAs vary widely. The 2026 reliability picture across major providers, with measured uptime and incident patterns.
torch.compile delivers big speedups when it works and weird breakage when it does not. The 2026 production guide for when to enable it.
Lightning vs raw PyTorch for production AI in 2026 — productivity, performance, and the trade-offs that matter at scale.
Activation checkpointing trades compute for memory. The 2026 PyTorch patterns and where the tradeoffs actually pay off.
Get notified when we publish new articles on AI voice agents, automation, and industry insights. No spam, unsubscribe anytime.
Try our live demo -- no signup required. Talk to an AI voice agent right now.