What if your AI could 'read' 100-page documents without forgetting the beginning?
Chunking strategies, RAG architectures, and memory management enable processing documents larger than context windows.
Eliminate 'forgotten' context issues, enable analy.
AI doesn't retrieve stored answers.
It predicts the next word. Then the next. Then the next — based on billions of real-world examples.
Given everything before this moment, what word comes next? That calculation, repeated hundreds of times, is your "response."
Understanding this changes how you prompt. Context is everything.
Wrong RAG type = failed project.
Naive → Simple Q&A
Advanced → Accuracy matters
Graph → Relationships
Agentic → Reasoning needed
Match complexity to solution.
🚀 Picking a vector database for your AI app? Let's break down the top 3! 🧠
🌲 Pinecone = zero ops, high cost
🦭 Milvus = open source, massive scale
🐘 pgvector = keep it simple with Postgres
Don't overcomplicate your stack! Choose based on your scaling needs and engineering bandwidth.
#VectorDB #AI #Pinecone #Milvus #pgvector #dougortiz
📌 Let's connect: https://www.linkedin.com/in/doug-ortiz-architect/
▶ YouTube: @techbits-do
🌐 Bio: https://bio.site/dougortiz
10 RAG types. Pick wrong = expensive failure.
NAIVE: Simple Q&A, 60% accuracy
ADVANCED: Reranking, 85% accuracy
GRAPH: Relationships, 90%+ complex queries
HYBRID: Vector+keyword, 25% better coverage
AGENTIC: Reasoning, 70% autonomous handling
MULTI-MODAL: Images/tables, unlocks 40% more data
Match RAG type to problem complexity.
AI is not free. ROI isn't automatic.
Costs: per-use, integration, talent, oversight.
Value: time saved, quality, new capabilities.
Prioritize: frequent tasks, valuable time, quality matters.
Avoid: infrequent, minimal savings, cost > benefit.
Match investment to value.
25 prompt patterns in 6 categories:
ACCURACY: Unicorn, Chain-of-Thought, Self-Consistency, ReAct, Fact Check
REFINEMENT: Critic, Reverse Questions, Flipped Interaction, Devil's Advocate, Reflection
OUTPUT: Template, Few-Shot, Persona, Audience, Recipe
EXPLORATION: 3 Lenses, Visualization, Expansion, Summarization, Filter
ADVANCED: Meta-Prompt, Game Play, Menu Actions, Automator, Extraction
Each has: Use When + Template + Watch For.
AI can sound completely convincing and still be wrong.
The "confident intern" problem:
• Doesn't say "I don't know"
• Improvises answers that sound good
• Equally confident when right OR wrong
Never trust AI for critical facts without verification.
What if your $2M AI investment runs at 20% capacity because your agents can't talk to each other?
73% of agent architectures I review have the hub-and-spoke problem: one orchestrator, keyword routing, zero agent-to-agent communication. Multi-step workflows? Impossible.
The fix: A2A protocol: agents that discover, delegate, and collaborate. Alongside MCP (agent↔tool), you get a digital workforce.
What's your agent architecture bottleneck?
What if catching bad data at the source was faster than fixing it downstream?
Implement quality gates at pipeline entry points: schema validation, statistical drift detection, and freshness checks t
95% issue prevention. 40% less debugging time.