AI Prompt Engineering · Lesson

Hybrid: Prompt + Light Tuning

Combining both approaches.

The Hybrid Mindset

Prompting and fine-tuning are not rivals - they are layers. The expert pattern is a lightly tuned model that handles stable, learned behavior, wrapped in a thin prompt that supplies the volatile, per-request context.

Tuning absorbs what is stable: format, voice, task framing
Prompting supplies what is volatile: instructions, retrieved facts, user state
Each layer does what it is best at; neither carries the whole load

Stable vs Volatile Decomposition

The core hybrid skill is splitting behavior along the stable/volatile axis. Anything that is identical across calls and expensive to repeat in the prompt is a tuning candidate. Anything that changes per call must stay in the prompt.

Get this split wrong in either direction and you lose: bake volatile content into weights and you must re-tune to change it; keep stable content in the prompt and you pay its token tax forever.

# Classify each behavior element before deciding where it lives
def placement(element):
    # element: dict with 'changes_per_call' and 'repeated_every_call'
    if element['changes_per_call']:
        return 'PROMPT'        # volatile -> must stay dynamic
    if element['repeated_every_call']:
        return 'WEIGHTS'       # stable + repetitive -> distill into tuning
    return 'PROMPT'            # default to the flexible layer

print(placement({'changes_per_call': False, 'repeated_every_call': True}))
# WEIGHTS

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