Editing Prompt Engineering (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Prompting Technique Comparison
! Technique !! Best For !! Limitation
|-
| Zero-shot || Simple, well-defined tasks || Fails on novel or complex reasoning
|-
| Few-shot || Format enforcement, stylistic tasks || Uses context tokens; examples must be high quality
|-
| Chain-of-thought || Math, logic, multi-step reasoning || Longer responses; still can reason incorrectly
|-
| Self-consistency || High-stakes factual questions || Expensive (multiple API calls)
|-
| Role prompting || Tone, domain vocabulary, expertise level || Model may not fully "become" the role
|-
| Tree of Thoughts || Open-ended problems requiring search || Very expensive; complex to implement
|}

'''Common failure modes:'''
* '''Ambiguous instructions''' — "Summarize this" without specifying length, audience, or focus produces wildly variable outputs. Be explicit.
* '''Prompt injection''' — If user input is inserted into a prompt without sanitization, attackers can override system instructions ("Ignore all previous instructions and…").
* '''Over-constraining''' — Too many rules in the system prompt create conflicts; the model satisfies some at the expense of others. Prioritize ruthlessly.
* '''Sycophancy through examples''' — If few-shot examples are biased toward one answer type, the model will mirror that bias rather than reasoning independently.
* '''Token overflow''' — Stuffing too much context causes the model to lose track of early instructions (the "lost in the middle" problem). Put critical instructions at the beginning and end of the prompt.
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