Learning Paths
Knowledge Base
Structured tutorials and reference knowledge—organized for learning and lookup
How I Cut Prompt Latency by 81% and Reduced Token Spend by 62% with Schema-Driven Compilation
Current Situation Analysis In production, LLM integration is rarely a chatbot demo. It’s a high-throughput data pipeline where prompts are serialized, validated, compressed, and executed against strict SLAs. Most teams treat prompts as freeform strings assembled at runtime.
Cutting LLM Inference Costs by 64% and Latency by 48% with Speculative-First Routing and KV-Cache Overcommit
Current Situation Analysis We migrated our LLM serving layer from a naive round-robin load balancer to a specialized infrastructure in Q3 2024. The results were not incremental; they were structural. We reduced cost per million output tokens from $3.80 to $1.36, cut p99 latency from 1.4s to 0.
Cutting Ollama Cold Start Latency by 92% and Reducing GPU Costs by 40% with Dynamic Model Routing and vRAM Optimization
Current Situation Analysis Most engineering teams treat Ollama as a drop-in replacement for OpenAI in development and hit a wall immediately in production. The standard tutorial pattern is docker run ollama/ollama followed by setting OLLAMA_KEEP_ALIVE=-1.
Slashing RAG Costs by 64% and Latency to 180ms with Semantic Caching and Adaptive Chunking
Current Situation Analysis When we audited our internal RAG pipelines across three product lines, the results were embarrassing. We were burning $14,000/month in LLM inference costs for a system with 42% cacheable query overlap.
Customer development interviews
## Current Situation Analysis Customer development interviews are the primary feedback mechanism between engineering output and market reality. Despite their critical role, they remain one of the most
Cutting LLM API Spend by 62% and P99 Latency by 450ms with Semantic Request Coalescing and Adaptive Context Pruning
Current Situation Analysis We migrated our customer support agent to an LLM-driven architecture six months ago. Within three weeks, the API bill hit $18,000/month, and our P99 latency jittered between 800ms and 2.4s. The root cause wasn't the model choice; it was how we treated the API.
The Cohort-Atomic Rollback Pattern: Cutting PMF Validation Time by 94% and Saving $140k/Month in Compute Waste
Current Situation Analysis Most engineering teams treat Product-Market Fit (PMF) as a retrospective business analysis. You build a feature, deploy it to 100% of users, wait three weeks for analytics to aggregate, and then decide if it "worked." This latency is catastrophic.