Prompt Engineering - Revision history

Wordpad: BloomWiki: Prompt Engineering

2026-04-25T01:56:16Z

BloomWiki: Prompt Engineering

← Older revision		Revision as of 01:56, 25 April 2026
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			<div style="background-color: #4B0082; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
	{{BloomIntro}}		{{BloomIntro}}
	Prompt engineering is the practice of designing, refining, and optimizing the inputs given to large language models (LLMs) to elicit desired outputs reliably and efficiently. Because LLMs are extraordinarily sensitive to how questions are framed, the prompt is effectively the user interface between human intent and model capability. A well-crafted prompt can unlock reasoning abilities, enforce output formats, dramatically reduce hallucinations, and transform a mediocre response into an expert-level one — without changing the model at all. Prompt engineering is part art, part science, and increasingly a critical professional skill.		Prompt engineering is the practice of designing, refining, and optimizing the inputs given to large language models (LLMs) to elicit desired outputs reliably and efficiently. Because LLMs are extraordinarily sensitive to how questions are framed, the prompt is effectively the user interface between human intent and model capability. A well-crafted prompt can unlock reasoning abilities, enforce output formats, dramatically reduce hallucinations, and transform a mediocre response into an expert-level one — without changing the model at all. Prompt engineering is part art, part science, and increasingly a critical professional skill.
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	== Remembering ==		__TOC__

			<div style="background-color: #000080; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Remembering</span> ==
	* '''Prompt''' — The text input provided to an LLM; the complete context from which the model generates a response.		* '''Prompt''' — The text input provided to an LLM; the complete context from which the model generates a response.
	* '''System prompt''' — A special instruction block (usually at the start of a conversation) that defines the model's role, persona, constraints, and output expectations.		* '''System prompt''' — A special instruction block (usually at the start of a conversation) that defines the model's role, persona, constraints, and output expectations.
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	* '''ReAct''' — A prompting framework combining reasoning traces ("Thought:") and tool calls ("Action:") for agent-like behavior.		* '''ReAct''' — A prompting framework combining reasoning traces ("Thought:") and tool calls ("Action:") for agent-like behavior.
	* '''Constitutional prompting''' — Including explicit rules or principles in the prompt to constrain the model's behavior.		* '''Constitutional prompting''' — Including explicit rules or principles in the prompt to constrain the model's behavior.
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	== Understanding ==		<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Understanding</span> ==
	LLMs are next-token predictors — they complete text in a manner consistent with their training distribution. A prompt is not a command but a '''context that constrains the probability distribution''' over possible continuations. When you write a clear, structured prompt, you are steering the model toward the region of its output space that contains useful, accurate responses.		LLMs are next-token predictors — they complete text in a manner consistent with their training distribution. A prompt is not a command but a '''context that constrains the probability distribution''' over possible continuations. When you write a clear, structured prompt, you are steering the model toward the region of its output space that contains useful, accurate responses.

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	The more precisely each of these components is specified, the less the model must infer — and inference is where errors enter.		The more precisely each of these components is specified, the less the model must infer — and inference is where errors enter.
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	== Applying ==		<div style="background-color: #8B0000; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Applying</span> ==
	'''Prompt templates for common use cases:'''		'''Prompt templates for common use cases:'''

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	: '''Generated Knowledge''' → Ask model to generate relevant facts first, then answer		: '''Generated Knowledge''' → Ask model to generate relevant facts first, then answer
	: '''Directional Stimulus''' → Include a hint or keyword that steers output direction		: '''Directional Stimulus''' → Include a hint or keyword that steers output direction
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	== Analyzing ==		<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Analyzing</span> ==
	{\| class="wikitable"		{\| class="wikitable"
	\|+ Prompting Technique Comparison		\|+ Prompting Technique Comparison
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	* '''Sycophancy through examples''' — If few-shot examples are biased toward one answer type, the model will mirror that bias rather than reasoning independently.		* '''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.		* '''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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	== Evaluating ==		<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Evaluating</span> ==
	Expert prompt engineers evaluate prompts systematically, not by feel:		Expert prompt engineers evaluate prompts systematically, not by feel:

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	Expert practitioners maintain a '''prompt changelog''' — every production prompt change is documented with before/after performance metrics, the rationale for the change, and rollback instructions.		Expert practitioners maintain a '''prompt changelog''' — every production prompt change is documented with before/after performance metrics, the rationale for the change, and rollback instructions.
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	== Creating ==		<div style="background-color: #2F4F4F; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Creating</span> ==
	Designing a robust prompt engineering workflow:		Designing a robust prompt engineering workflow:

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	[[Category:Large Language Models]]		[[Category:Large Language Models]]
	[[Category:Prompt Engineering]]		[[Category:Prompt Engineering]]
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Wordpad: BloomWiki: Prompt Engineering

2026-04-23T14:35:12Z

BloomWiki: Prompt Engineering

← Older revision		Revision as of 14:35, 23 April 2026
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	* '''Prompt injection''' — An attack where malicious input in the prompt attempts to override the model's original instructions.		* '''Prompt injection''' — An attack where malicious input in the prompt attempts to override the model's original instructions.
	* '''Hallucination''' — The model generating plausible-sounding but factually incorrect content; prompt techniques can mitigate but not fully eliminate this.		* '''Hallucination''' — The model generating plausible-sounding but factually incorrect content; prompt techniques can mitigate but not fully eliminate this.
	* '''Delimiters''' — Characters or tags (e.g., ~~```~~triple backticks~~```~~, <tags>, ---) used to clearly separate sections of a prompt.		* '''Delimiters''' — Characters or tags (e.g., <code>triple backticks</code>, <tags>, ---) used to clearly separate sections of a prompt.
	* '''ReAct''' — A prompting framework combining reasoning traces ("Thought:") and tool calls ("Action:") for agent-like behavior.		* '''ReAct''' — A prompting framework combining reasoning traces ("Thought:") and tool calls ("Action:") for agent-like behavior.
	* '''Constitutional prompting''' — Including explicit rules or principles in the prompt to constrain the model's behavior.		* '''Constitutional prompting''' — Including explicit rules or principles in the prompt to constrain the model's behavior.

Wordpad: New article: Prompt Engineering structured through Bloom's Taxonomy

2026-04-23T06:27:58Z

New article: Prompt Engineering structured through Bloom's Taxonomy

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Wordpad: Add BloomIntro banner

2026-04-23T05:43:25Z

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← Older revision		Revision as of 05:43, 23 April 2026
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	{{Bloom Article}}		{{Bloom Article}}
	Prompt engineering is the practice of crafting inputs to AI language models in order to elicit accurate, useful, and reliable outputs. As large language models (LLMs) become embedded in software, research, and creative workflows, knowing how to communicate with them effectively has become a foundational skill.		Prompt engineering is the practice of crafting inputs to AI language models in order to elicit accurate, useful, and reliable outputs. As large language models (LLMs) become embedded in software, research, and creative workflows, knowing how to communicate with them effectively has become a foundational skill.

Wordpad: Initial article: Prompt Engineering structured through Bloom's Taxonomy

2026-04-23T05:29:08Z

Initial article: Prompt Engineering structured through Bloom's Taxonomy

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{{Bloom Article}}
Prompt engineering is the practice of crafting inputs to AI language models in order to elicit accurate, useful, and reliable outputs. As large language models (LLMs) become embedded in software, research, and creative workflows, knowing how to communicate with them effectively has become a foundational skill.

== Remembering ==
A '''prompt''' is any text (or structured input) given to a language model to direct its response. Key terms:

* '''LLM''' (Large Language Model) � an AI system trained on large text corpora to predict and generate language (e.g., GPT-4, Claude, Gemini).
* '''Token''' � the unit LLMs process; roughly � of a word on average.
* '''System prompt''' � instructions given to the model before the user turn, often to set role or constraints.
* '''User prompt''' � the human-authored input in a conversation turn.
* '''Context window''' � the maximum number of tokens an LLM can process in one call.
* '''Temperature''' � a parameter controlling output randomness; higher = more creative, lower = more deterministic.
* '''Zero-shot''' � prompting without examples.
* '''Few-shot''' � prompting with a small number of examples embedded in the prompt.
* '''Chain-of-thought (CoT)''' � instructing the model to reason step by step before giving a final answer.

== Understanding ==
LLMs are next-token predictors: given a sequence of tokens, they assign probabilities to what comes next, then sample from that distribution. Prompt engineering works because the model has learned patterns from its training data � the way a prompt is framed shifts which patterns the model activates.

Several mechanisms explain why certain prompting strategies succeed:

* '''Priming''' � early tokens in the context bias the distribution of later tokens. Opening with "You are an expert..." shifts the model toward confident, technical language.
* '''In-context learning''' � few-shot examples effectively demonstrate the desired input-output format without updating model weights.
* '''Chain-of-thought reasoning''' � breaking complex tasks into explicit steps reduces the chance of the model "shortcutting" to a plausible but wrong answer.
* '''Role assignment''' � setting a persona frames which knowledge and tone the model draws on.

The model does not "understand" instructions the way humans do; it pattern-matches. This means precise, unambiguous language outperforms vague or colloquial phrasing.

== Applying ==
Common prompt patterns used in practice:

; Instruction + context + format
: State what you want, give necessary background, and specify the output structure. Example: "Summarize the following contract clause in plain English, using three bullet points, each under 20 words: [clause text]"

; Role prompting
: "You are a senior tax attorney. Review the following scenario and identify compliance risks."

; Few-shot with worked examples
: Provide 2�5 input/output pairs before the real query so the model learns the desired format implicitly.

; Chain-of-thought
: Append "Think step by step" or provide a reasoning skeleton the model fills in.

; Self-consistency
: Generate multiple responses at higher temperature, then pick the answer that appears most often � useful for math and logic problems.

; Retrieval-augmented prompting
: Insert retrieved documents or database results into the prompt so the model grounds its answer in current or private data rather than training memory.

Practical workflow: start with the simplest prompt that could work, test it on diverse inputs, iterate on failures, then lock the working version as a template.

== Analyzing ==
Prompt quality depends on several interacting factors:

{| class="wikitable"
|-
! Factor !! Effect when optimized !! Common failure mode
|-
| Specificity || Model knows exactly what to produce || Underspecified prompts produce generic, hedged answers
|-
| Ordering || Most important instructions early or at the very end (primacy/recency effects) || Critical constraints buried in the middle are ignored
|-
| Length || Enough context for accuracy || Overly long prompts dilute attention; key instructions get lost
|-
| Examples || Anchor format and tone || Unrepresentative examples mislead the model about edge cases
|-
| Constraints || Reduce unwanted outputs || Contradictory constraints cause unpredictable behavior
|}

A key limitation: prompts are not programs. The same prompt can yield different outputs across model versions, temperatures, or even repeated calls at the same temperature. Robust systems treat prompts as probabilistic policies, not deterministic functions, and test them statistically across a sample of inputs.

Another limitation is '''context window pressure''': as prompts and conversation history grow, older content falls outside the context or receives less attention, degrading instruction-following.

== Evaluating ==
Expert prompt engineers judge prompts against three criteria simultaneously:

# '''Accuracy''' � does the output answer the actual question correctly?
# '''Reliability''' � does it do so consistently across varied phrasings and edge cases?
# '''Efficiency''' � does it achieve this with minimal tokens (cost) and latency?

Advanced strategies:

* '''Prompt versioning''' � treat prompts like code: store them in version control, log which version produced which output, and roll back on regressions.
* '''Eval-driven iteration''' � build a labeled test set of inputs and expected outputs before writing the prompt. Score every candidate prompt against the eval. This prevents overfitting a prompt to the one or two examples you happened to test by hand.
* '''Meta-prompting''' � ask the LLM to critique and rewrite its own prompt given the failure cases you observed.
* '''Constitutional prompting''' � embed a set of explicit principles the model should self-check against before finalizing its answer.
* '''Guard-railing''' � add a validation step (another LLM call or a deterministic check) that inspects the output before it reaches the user and re-prompts if it fails.

The single most common expert mistake is optimizing a prompt on the training distribution (the cases you thought of) rather than validating on held-out or adversarial inputs.

== Creating ==
Designing a prompt system � rather than a single prompt � requires treating prompting as software architecture:

; Modular prompt templates
: Separate the static skeleton (role, format, constraints) from dynamic slots (user query, retrieved context, tool outputs). This lets you swap components independently.

; Prompt chaining
: Decompose complex tasks into a pipeline of smaller prompts whose outputs feed the next stage. Example: (1) extract key entities ? (2) retrieve relevant docs ? (3) draft answer ? (4) critique and revise.

; Agentic loops
: The model iteratively calls tools (search, code execution, databases), inspects results, and decides the next action. Prompts here must define the tool schema, the stopping condition, and how the model should handle tool errors.

; Evaluation harnesses
: A production prompt system ships with an automated eval harness � a suite of test cases, scoring functions, and a regression dashboard � so that any prompt change triggers a quality gate before deployment.

; Cost/quality frontier
: Map the trade-off between model size/cost and output quality for your specific task. Often a smaller model with a well-engineered prompt outperforms a larger model with a naive prompt at a fraction of the cost.

Designing for robustness means assuming the model will occasionally fail and building the surrounding system (retries, fallbacks, human-in-the-loop escalation) to handle that gracefully rather than expecting the prompt alone to be sufficient.

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Productivity]]