Fine-Tuning vs Prompting vs RAG: When to Adapt a Model
Model Adaptation & Inference Optimization: leave with a LoRA fine-tune with before/after evals and a quantization speed/quality comparison.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
1:1 or squad batch · join the community or channel
Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why model adaptation & inference optimization matters — the common problem: Teams fine-tune when prompting would do, or serve models far slower and costlier than needed.
- 2Core concepts in plain language: Fine-tuning, supervised fine-tuning, parameter-efficient fine-tuning, LoRA, preference optimization, reinforcement learning from feedback
- 3Going further: distillation, quantization, pruning, batching, KV caching, speculative decoding
- 4Framework walkthrough: Transformer Architecture, Prompt → Retrieve → Generate → Verify, Evaluation Sets
- 5Practical workflow, built live: A LoRA fine-tune with before/after evals and a quantization speed/quality comparison.
- 6How to measure it: Groundedness, Answer accuracy on an eval set, Latency, Cost per request
- 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 LoRA fine-tune with before/after evals and a quantization speed/quality comparison.
- A working understanding of Transformer Architecture and Prompt → Retrieve → Generate → Verify
- A short list of measures to track: Groundedness, Answer accuracy on an eval set, Latency
More topics in IT: AI & LLM Engineering
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.
0 votes · View topic →
Machine Learning Fundamentals Without the Hype
Machine Learning: leave with a cross-validated model versus a baseline, with a leakage check and error analysis.
0 votes · View topic →
Deep Learning Explained: From Neurons to Transformers
Deep Learning: leave with a small network trained from scratch, loss curves, and a transfer-learning comparison.
0 votes · View topic →
Reinforcement Learning and Bandits in Plain Language
Reinforcement Learning & Decision Optimization: leave with a Q-learning agent on a toy environment and a bandit simulation with exploration analysis.
0 votes · View topic →
Applied ML: Vision, Forecasting, Recommendations and More
Applied Machine Learning Specializations: leave with a working forecast or anomaly-detection model with a problem-to-technique mapping note.
0 votes · View topic →
Generative AI Explained: LLMs, Diffusion and Beyond
Generative AI & Foundation Models: leave with a comparison sheet of model families with a hands-on text and image generation test.
0 votes · View topic →