AI Before LLMs: Search, Rules and Knowledge Graphs
AI Foundations & Classical AI: leave with a constraint-satisfaction or A* solution and a note on when classical AI beats LLMs.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai foundations & classical ai matters — the common problem: Every problem gets thrown at an LLM when a simple search or rule system would be exact.
- 2Core concepts in plain language: Intelligent agents, symbolic AI, heuristic search, planning, knowledge representation, rule-based systems
- 3Going further: expert systems, constraint satisfaction, probabilistic reasoning, knowledge graphs, neuro-symbolic approaches
- 4Framework walkthrough: CRISP-DM, Train / Validation / Test Splits, Cross-Validation
- 5Practical workflow, built live: A constraint-satisfaction or A* solution and a note on when classical AI beats LLMs.
- 6How to measure it: Accuracy, precision and recall, Generalization gap, Baseline comparison
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A constraint-satisfaction or A* solution and a note on when classical AI beats LLMs.
- A working understanding of CRISP-DM and Train / Validation / Test Splits
- A short list of measures to track: Accuracy, precision and recall, Generalization gap, Baseline comparison
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