Structured tutorials and reference knowledge—organized for learning and lookup
Current Situation Analysis RAG evaluation is the silent cost center in production AI. Most teams treat evaluation as a batch benchmark: run RAGAS 0.2.1 or LangSmith against a static dataset, collect faithfulness and answer relevance scores, and ship. This works for 50 examples.
Current Situation Analysis When we migrated our internal knowledge base to an LLM-driven architecture, our initial indexing pipeline looked like every tutorial on the internet: split text into fixed 512-token chunks, call the embedding API, and dump vectors into Pinecone.
Current Situation Analysis Most engineering teams treat Retrieval-Augmented Generation (RAG) as a single retrieval step: chunk text, embed it, store in a vector database, and run similarity search.
Current Situation Analysis Building an AI SaaS product in 2024-2025 isn’t about wrapping an LLM API. It’s about surviving the unit economics of inference. Most teams start with a synchronous FastAPI endpoint that accepts a prompt, forwards it to OpenAI or Anthropic, and returns the response.
Current Situation Analysis Most product analytics setups are architectural liabilities. You install a third-party SDK, sprinkle track() calls throughout your UI, and hope for the best. When traffic spikes, your analytics script blocks the main thread, increasing Time to Interactive (TTI).
Current Situation Analysis Most engineering teams price AI features using static rate cards: $0.002 per input token, $0.006 per output token, or a flat $49/month tier. This model collapses under production load because AI inference costs are not linear.
Current Situation Analysis Most engineering teams treat MVP validation as a business exercise disguised as deployment. You spin up a staging environment, wait for organic traffic, manually grep CloudWatch or Datadog logs, and hope the conversion metrics justify the build.
Current Situation Analysis We migrated our semantic search pipeline from OpenAI's text-embedding-3-small to a local quantized model six months ago. The motivation wasn't just privacy; it was unit economics.
Current Situation Analysis Most engineering teams treat A/B testing as a static configuration problem. They deploy a 50/50 split, wait 14 days for statistical significance, and manually roll out the winner.
Current Situation Analysis LM Studio 0.3.5 is an exceptional tool for local development and rapid prototyping. Its GUI abstraction over llama.cpp allows developers to load GGUF models, tweak parameters, and get inference running in minutes.
Current Situation Analysis Customer development (CustDev) is treated as a product management ritual, not an engineering system. Teams schedule 30-minute interviews, transcribe them manually, tag quotes in Notion, and wait two weeks for a synthesis report.
Current Situation Analysis Ollama is an exceptional tool for local development, but treating it as a drop-in production service is a recipe for instability.
