Your Guide to Prompt Engineering: 9 Techniques You Should Know

A practical guide to structuring better prompts — from zero-shot to advanced reasoning and agent-based strategies

Photo by Emiliano Vittoriosi on Unsplash

Prompt engineering is a core skill for anyone interacting with Large Language Models (LLMs). The way you formulate your prompts can dramatically affect the quality, consistency, and usefulness of the outputs you get from models like GPT-4, Claude, or Gemini.

As LLMs become more integrated into workflows, the ability to craft effective prompts directly translates to improved efficiency, accuracy, and innovation across industries. However, effectively prompting LLMs is not always straightforward. The nuances of language, the potential for ambiguity, and the variability in LLM responses create a need for structured techniques to elicit desired outcomes.

This article provides a comprehensive overview of 9 essential prompting techniques, summarized and adapted from Lee Boonstra’s Prompt Engineering report. Each technique represents a different strategy for guiding LLMs — from simple instructions without examples to structured reasoning paths and agent-like behaviors.

We’ll explore:

• When to use each technique
• Why it works (based on how LLMs process information)
• Practical examples for real-world tasks
• Trade-offs and edge cases

These techniques will help you unlock more consistent, accurate, and controllable outputs.

Zero-shot Prompting

✅ Definition

Zero-shot prompting refers to issuing a prompt to a LLM without providing any examples of the desired output. The model relies solely on its pretrained knowledge to interpret the task and generate a response.

🧠 Why it works

LLMs like GPT-4 and Claude have been trained on massive text corpora and have learned task patterns implicitly. Even without context or demonstrations, they can often infer the intent of your instruction if it’s clearly phrased.

📌 When to use

• The task is straightforward (e.g., summarization, translation, classification)
• You want a quick test or prototype
• The required output is deterministic or well-known

💬 Example

Prompt:

“Summarize the following email in two bullet points.”

Expected output:

• Meeting has been rescheduled to Friday at 2 PM.
• Presentation slides are due Thursday EOD.

⚠️ Limitations

• May lack structure or consistency in complex tasks
• Less controllable without examples
• Not suitable for nuanced formatting

One-shot and Few-shot Prompting

✅ Definition

These techniques involve providing one (one-shot) or a few (few-shot) examples of the desired output format or behavior within the prompt. This anchors the model and helps it mimic the desired structure or logic.

🧠 Why it works

LLMs use in-context learning — they can pick up on patterns from the examples and apply them to new inputs. This improves format consistency and output alignment.

📌 When to use

• The task benefits from pattern imitation
• Outputs need to follow a specific template or logic
• You want to steer the model toward a known style

💬 Example

Prompt:

Feature: Single Sign-On
Effort: Medium (6 weeks)
Impact: High — requested by 70% of prospects
Alignment: 9/10

Please create similar cards for these features: API integration, mobile app

Expected output:

Feature: API Integration
Effort: High (8 weeks)
Impact: High — required for partnerships
Alignment: 8/10
…

⚠️ Limitations

• Examples may bias the model excessively
• Prompts get long and harder to manage
• Errors can propagate if examples are poor

System, Role, and Context Priming

✅ Definition

These are framing strategies that shape the model’s response by simulating identity or environment:

• System: Defines how the model should behave or format output
• Role: Assigns a persona (e.g., expert, mentor)
• Context: Provides background facts

🧠 Why it works

These inputs alter the internal reasoning of the model. Think of it as setting up the rules of the simulation before you start the interaction.

📌 When to use

• You want structured or expert-like outputs
• You need the model to stay aligned with business context
• You want the model to imitate human roles

💬 Example

Prompt:

System: You are a senior product manager.
Role: You are preparing talking points for the CEO.
Context: Revenue is flat. Churn is up. A big release is delayed.
Prompt: Write a 5-bullet summary for the board.

Expected output:

• Q2 revenue remained stable at $12M
• Churn increased 1.4%, primarily in SMB segment
  …

⚠️ Limitations

• Too much context can confuse the model
• Requires precise setup for consistency

Step-back Prompting

✅ Definition

Step-back prompting involves asking a broader or more abstract question first before narrowing down to the specific task. This encourages the model to activate general knowledge before applying it.

🧠 Why it works

By “zooming out,” the model taps into its conceptual and contextual understanding of the domain. This leads to more grounded and insightful outputs when the actual task is introduced.

📌 When to use

• Creative tasks requiring ideation
• Strategy formulation or abstract reasoning
• Need for foundational knowledge before generating an answer

💬 Example

Prompt 1 (Step-back):

“What makes a SaaS free trial successful?”

Prompt 2 (Follow-up):

“Now write a landing page headline that reflects those success factors.”

Expected output:

“Start Your 14-Day Free Trial — No Credit Card Needed, Full Feature Access, Cancel Anytime.”

⚠️ Limitations

• Requires multi-step orchestration
• Not as effective for routine or well-defined tasks

Chain-of-Thought (CoT)

✅ Definition

CoT prompting encourages the model to think step-by-step, revealing the logic behind its answer. It can be used explicitly (“let’s think step by step”) or implicitly by formatting examples that show intermediate reasoning.

🧠 Why it works

This technique aligns with how LLMs sequence text: when reasoning steps are explicitly written out, the model is more likely to arrive at a correct and explainable result.

📌 When to use

• Logic-heavy tasks (math, diagnostics, root cause analysis)
• Problems that benefit from intermediate steps
• When you want transparent reasoning paths

💬 Example

Prompt:

“Our churn rate is up. Let’s think step-by-step: What could be causing it, and what data should we look at?”

Expected output:

1. Product issues? → Check support ticket volumes
2. Competitor movement? → Analyze recent pricing changes
3. Customer satisfaction? → Review latest NPS survey
   …

⚠️ Limitations

• Can increase response length
• Sometimes leads to hallucinated steps if not properly constrained

Self-Consistency Prompting

✅ Definition

This involves running the same prompt multiple times and comparing the outputs. The model can then evaluate its own responses or a human can select the most consistent or well-reasoned answer.

🧠 Why it works

LLMs are stochastic — they generate different outputs with each run. By sampling multiple completions and selecting the best, we can approximate consensus or quality through diversity.

📌 When to use

• High-stakes outputs (e.g., analytics, summarization)
• Tasks where multiple reasoning paths are valid
• When confidence and correctness matter

💬 Example

Prompt:

“What’s the most likely reason for a drop in product-qualified leads last month? Explain your reasoning.”

→ Run this 5 times
→ Grade each answer on completeness and clarity
→ Return the best-rated one

Expected output (final answer):

“Drop in PQLs was likely caused by a broken onboarding flow after the website redesign. Analytics show a 40% increase in bounce rates on the signup page starting March 3rd.”

⚠️ Limitations

• Requires automation or manual grading
• Resource-intensive (multiple API calls)

Tree-of-Thought (ToT)

✅ Definition

ToT prompting asks the model to branch out into multiple reasoning paths in parallel, instead of following a single linear chain. The model then explores each branch before synthesizing an answer.

🧠 Why it works

This mirrors decision trees or strategic analysis used in human reasoning. It helps uncover more creative or overlooked ideas and balances trade-offs between options.

📌 When to use

• Complex decision-making
• Exploratory analysis (e.g., product, UX, risk mitigation)
• Tasks with many possible solutions

💬 Example

Prompt:

“Brainstorm multiple approaches for reducing user friction in onboarding. Expand each with pros and cons. Recommend the best one.”

Expected output:

1. Simplify form fields — ✅ faster signup; ❌ less qualified leads
2. Add progress bar — ✅ sets expectations; ❌ may distract
3. Onboarding checklist — ✅ improves task completion; ❌ UX clutter
   → Recommendation: Combine 1 & 3

⚠️ Limitations

• Output can be verbose
• Requires structured formatting for clarity

ReAct (Reason + Act)

✅ Definition

This method combines reasoning with external actions. The LLM thinks, performs a real-world action (like a search), and then updates its reasoning with new information. Common in agent-based systems.

🧠 Why it works

ReAct simulates how humans solve problems — thinking, gathering data, re-evaluating, and then deciding. It allows LLMs to operate in dynamic environments using tools or APIs.

📌 When to use

• Tasks involving real-time or external data
• Multi-step tool usage
• Building LLM agents or assistants

💬 Example

Prompt:

“Search LinkedIn for the latest ‘Head of Product’ job listings in B2B SaaS. Summarize the most common skill requirements.”

Expected output:

1. [Action] → Performs search
2. [Observation] → Collects job descriptions
3. [Reasoning] → Synthesizes common themes
4. [Answer] → “Top 3 skills: cross-functional leadership, customer-centric roadmap planning, data fluency”

⚠️ Limitations

• Requires integration with tools/APIs
• Not natively supported in vanilla LLM interfaces

Automatic Prompt Engineering (APE)

✅ Definition

APE involves using the LLM to generate, evaluate, and refine its own prompts. Instead of manually crafting a prompt, you ask the model to try different versions and score them for quality.

🧠 Why it works

By running prompt iterations, APE leverages the LLM’s own ability to understand what leads to better outcomes. It functions like prompt A/B testing, enabling self-improvement.

📌 When to use

• Building reusable, optimized prompts
• When prompt phrasing impacts output quality
• Scaling prompt development workflows

💬 Example

Prompt:

“Generate 10 different prompts for extracting themes from customer feedback. Score them for clarity and effectiveness.”

Expected output:

1. “Identify common topics in the following feedback…” — Score: 9/10
2. “Summarize key pain points mentioned in these reviews…” — Score: 8.5/10
   …

→ Select top 3 for testing

⚠️ Limitations

• Needs structured scoring criteria
• May over-optimize for internal logic rather than external results

Final Thoughts

Prompting techniques are part of a larger system for getting the most out of language models. Each technique brings its own strengths, and when used thoughtfully, they allow you to:

• Increase accuracy and reliability
• Guide reasoning processes
• Customize tone, format, or output quality
• Scale and automate workflows

Like other disciplines, prompt engineering is iterative. You test, tweak, evaluate, and evolve. Whether you’re summarizing legal documents, generating marketing copy, or building LLM-powered tools, mastering these techniques will help you move from “hacking prompts” to designing systems.

Finally, considering prompting techniques in isolation misses the opportunity to discuss their synergistic potential. In real-world applications, prompt engineering is rarely a one-size-fits-all endeavor. Instead, effective workflows often involve a carefully orchestrated sequence of prompts, each employing a different technique to achieve a specific objective. For example, we might use Role Prompting to set the context, followed by Chain-of-Thought to decompose a complex task, and finally Self-Consistency to refine the output. This ensemble of techniques works in concert to optimize the entire process, from initial input to final result, streamlining development and enhancing overall efficiency.

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