🌍
Real Unknown
Stage 1 — The Why · OKRs, problem definitions, and core questions
This stage captures the core problem we're solving and defines measurable success criteria via OKRs. Everything in the journey maps back to these objectives.
🎯
Objectives & Key Results
🎯 Objective 1: Master Local LLM Deployment with Ollama
- 1Successfully install and configure Ollama on all supported platforms (Linux, macOS, Windows)
- 2Deploy and run at least 3 different language models locally
- 3Achieve stable performance with minimum 8GB RAM utilization
🛠 Objective 2: Develop Practical Applications
- 1Create 5 working examples using Ollama's API
- 2Implement a custom model integration project
- 3Document all implementations with comprehensive guides
⚡ Objective 3: Optimize Performance & Resource Usage
- 1Reduce model loading time by 25%
- 2Implement efficient memory management practices
- 3Achieve response times under 2 seconds for standard queries
📅
Timeline
Q1
Setup and basic implementation
Q2
Application development
Q3
Performance optimization
Q4
Documentation and refinement
❓
Core Questions
What we're trying to answer
🔒 Why Local?
Why run LLMs locally instead of using cloud APIs? Privacy, cost, latency, and offline capability.
🦙 Why Ollama?
What makes Ollama the right tool for local LLM deployment vs alternatives like LM Studio or llama.cpp?
🗄️ Why Qdrant?
How does a local vector database complement Ollama for semantic search and knowledge retrieval?
📊 What's the ROI?
How do we measure success? Response times, resource usage, model variety, and practical use cases built.