Practical guides on AI, LLMs, and vector databases. Learn cost math, RAG workflows, and vendor comparisons for micro teams.
Customization spectrum: prompts → RAG → fine-tuning → training AI customization exists on a spectrum of complexity and control: Most SaaS and enterprise teams live in steps 1–3. Choosing between prompts and fine-tuning depends on scale, consistency, and cost tolerance. Prompt engineering: real capabilities and limitations Prompt engineering is about getting more from the same […]
Fine-tuning vs. Prompt Engineering: When Each Wins Leer más »
Regulatory landscape: GDPR, CCPA, and sector-specific requirements Generative AI workflows operate at the intersection of data protection laws and emerging tech. Regulations like GDPR (EU) and CCPA (California) set broad rules around personal data, while sector-specific frameworks (HIPAA for healthcare, PCI DSS for finance, FERPA for education) add stricter requirements. Key obligations: Non-compliance risks include
Privacy & Compliance in Generative AI Workflows Leer más »
AI search promises vs traditional search reality AI search has been marketed as the silver bullet for discovery: users ask questions in natural language and magically get the perfect answer. In reality, traditional search (based on keywords, filters, and ranking algorithms) still powers most SaaS platforms because it’s predictable, fast, and explainable. The hype is
AI-powered Search for SaaS: What’s Hype vs. Reality Leer más »
Vector database landscape: when you need them vs PostgreSQL Vector databases are designed for similarity search on embeddings—turning unstructured data like text, images, or audio into searchable numeric vectors. They support operations like nearest neighbor search (kNN) with speed and scale that relational databases can’t match natively. You need a dedicated vector DB when: PostgreSQL
Vector DBs: Pinecone vs. Weaviate vs. Qdrant vs. pgvector Leer más »
LLM pricing models decoded: tokens, context, fine-tuning Understanding LLM costs starts with tokens. Tokens are chunks of text (≈4 chars or ¾ of a word). Pricing is usually measured in $ per 1,000 tokens. Three main factors drive costs: Additional pricing dimensions: Bottom line: total cost = input tokens + output tokens × price per
LLM Cost Math: OpenAI vs. Anthropic vs. Local Models Leer más »
RAG explained without jargon: what problem it actually solves Retrieval-Augmented Generation (RAG) is a way to make AI models more accurate by letting them “look things up” before answering. Instead of forcing an LLM to memorize everything, RAG connects it to an external knowledge base. The problem it solves: hallucinations and outdated answers. If you
RAG & Vectors for Non-ML Teams: A Practical Guide Leer más »
