5 AI Concepts
Understanding the 5 AI Concepts Every Evaluator and Development Professional Should Know
Practical AI foundations for Monitoring, Evaluation, Research, Learning, and Development professionals
Introduction
AI is now widely used in evaluation, research, and development work. It supports synthesis of reports, qualitative coding, literature reviews, and reporting. However, performance depends less on the tool itself and more on how information is structured and requested.
Input → Tokens → Context Window → Embeddings → Model Reasoning → Output → Workflow (Agents)
1. Tokens – How AI Reads and Breaks Down Information
AI does not read documents like humans. It breaks text into small units called tokens. A token may be a full word, part of a word, punctuation, or a symbol.
This means a long evaluation report is not processed as a whole document but as thousands of small pieces. The model reconstructs meaning from these pieces.
Common Error
Issue: No structure, no prioritization, and overload of information.
Improved Prompt
Advanced Prompt
– Positive outcomes
– Negative outcomes
– Unexpected outcomes
– Evidence gapsClassify each using OECD-DAC criteria and link each finding to supporting evidence.
Key insight: structured input reduces token waste and improves accuracy.
EvalCommunity Application
- Evaluation report review
- Qualitative coding
- Survey analysis
- Literature synthesis
2. Context Window – AI Working Memory Limit
The context window is the amount of information the AI can “see” at one time. It includes instructions, uploaded documents, and conversation history.
If information exceeds this limit, earlier content may be ignored or summarized implicitly.
Common Error
Issue: too many documents at once, no staged processing.
Improved Approach
Step 2: Extract key findings from each summary
Step 3: Compare themes across summaries
Step 4: Produce synthesis report
Visual Workflow
Report B → Summary B
Report C → Summary CAll summaries → Thematic comparison → Final synthesis
Key insight: AI performs better when complex tasks are broken into sequential steps.
3. Embeddings – Meaning-Based Understanding
Embeddings allow AI to understand meaning by converting text into mathematical representations. This enables similarity detection beyond exact words.
Common Error
Improved Prompt
Concept Expansion
EvalCommunity Application
- Thematic coding of qualitative data
- Literature reviews
- Evaluation evidence synthesis
- Knowledge management systems
Key insight: AI understands concepts, not just keywords.
4. Fine-Tuning – Specializing AI Models
Fine-tuning involves training a model on domain-specific datasets to improve performance. However, in most evaluation workflows, structured prompting achieves similar results.
Common Error
Improved Solution
– OECD-DAC framework
– Clear output templates
– Example-based guidance
Better Alternative Approach
EvalCommunity Application
- Standardized evaluation reporting
- Donor-aligned documentation
- Organizational learning systems
Key insight: prompts + examples often replace fine-tuning needs.
5. AI Agents – From Answers to Full Workflows
AI agents go beyond single responses. They perform structured multi-step workflows such as extraction, coding, synthesis, and reporting.
Common Error
Advanced Agent Prompt
Workflow Breakdown
2. Thematic coding
3. Cross-report comparison
4. Insight synthesis
5. Reporting and visualization
EvalCommunity Application
- Automated evaluation synthesis
- Qualitative coding assistants
- Report drafting systems
- Knowledge management workflows
Key insight: AI becomes significantly more powerful when used as a workflow system, not just a chatbot.
Key Takeaways
- Tokens control how AI processes text
- Context window defines working memory limits
- Embeddings enable semantic understanding
- Fine-tuning is often unnecessary in MEL practice
- AI agents enable full evaluation workflows
Mastering these concepts significantly improves the quality, structure, and reliability of AI outputs in monitoring, evaluation, and development work.
