2.4. Advanced Prompt Structures

Up to this point, we’ve focused on core prompting strategies: clear role, task, context, input, format, and tone. But for more complex tasks—reasoning, multi-step problem solving, or when accuracy matters—there are advanced structures that improve results. These methods build on the foundations of prompting and integrate research-based techniques to guide AI through logic, intermediate steps, or iterative refinement.

Key Advanced Techniques

1. Chain-of-Thought (CoT) Prompting

This technique asks the AI not just for an answer, but to show its reasoning process step by step. By exposing intermediate thinking, the AI is more likely to arrive at accurate conclusions. Research by Wei et al. (2022) demonstrated that CoT prompting significantly improves performance on arithmetic, commonsense, and symbolic reasoning tasks. Read the study.

• Example: “Reason step by step to design a 6-week Anterior Cruciate Ligament (ACL) rehab progression. Explain criteria for advancing from one phase to the next, then present the final program in a table.”
• Strengths: Useful for problem-solving and protocols requiring multiple layers of reasoning.
• Weaknesses: Can produce long, verbose outputs—clinicians must condense for patient-facing use (Wei et al., 2022).

2. Least-to-Most Prompting

Here, complex problems are broken down into a sequence of simpler subproblems. The AI solves them in order, using earlier steps to inform the next. This approach helps models generalize to harder tasks than they saw in examples. Read the study.

• Example: “Tackle this as substeps: (1) define the target audience for a 1-page policy brief on open educational resources, (2) list 5 key points with one citation each, (3) create a 6-bullet outline, (4) write the brief.”
• Strengths: Reduces cognitive load; helps the model generalize from easy to hard.
• Weaknesses: Requires you to specify good substeps; can take multiple turns.

3. Self-Consistency in Reasoning Paths

Rather than taking the first reasoning chain the model produces, self-consistency samples multiple chains and then selects the answer that appears most often. This reduces error in CoT prompting. Read the study.

• Example: “Generate 5 independent solutions (separate reasoning traces) to choose an LMS for a mid-sized university using criteria: cost, usability, integrations, support. Then vote for the best option and justify the choice.”
• Strengths: Improves reliability for reasoning tasks; reduces ‘first-path’ errors.
• Weaknesses: More tokens/time; you still review the final consensus for fit.

4. Tree of Thoughts (ToT)

Tree of Thoughts extends CoT by allowing the AI to explore branching reasoning paths—similar to trying out multiple strategies, checking different angles, then choosing the most promising. This is especially useful for planning or strategy tasks. Overview here.

• Example: “For improving first-year retention, branch possible strategies (curriculum, advising, financial support, belonging). For each branch, propose 2–3 tactics, score them on impact/feasibility (1–5), prune low scorers, and present the chosen plan with rationale.”
• Strengths: Excellent for planning, strategy, creative exploration, and trade-offs.
• Weaknesses: Can sprawl without guardrails; set evaluation criteria and limits.

5. Prompt Chaining and Multi-Turn Prompts

For complex tasks, it’s often effective to chain prompts: ask for part of the answer, check or refine, then continue. Multi-turn dialogue (where earlier AI responses or user feedback are incorporated) can also improve relevance and correctness.

• Example: “Step 1: Ask me 3 clarifying questions about a grant concept. Step 2: Propose two alternative outlines. Step 3: After I choose, draft a 300-word summary. Step 4: Revise based on my comments, then produce a final 1-page brief.”
• Strengths: Aligns outputs with evolving goals; builds quality through iteration.
• Weaknesses: Requires facilitation; can drift if you don’t keep scope tight.

6. Incorporating External Tools or Retrieval (RAG)

In Retrieval-Augmented Generation, you supply or allow access to external documents or sources, so the AI can ground its response in factual and current information. This helps avoid hallucinations (incorrect or invented content).

• Example: “Using the attached meeting minutes (Apr–Jun) and the two policy PDFs, draft a 400-word summary of our assessment plan. Cite document names and page numbers; if a claim isn’t found, say ‘insufficient evidence.’ Provide a references list.”
• Strengths: Reduces hallucinations; adds currency and verifiability.
• Weaknesses: Needs clean, relevant sources; poor docs → poor answers.

7. Zero-Shot Prompting

A zero-shot prompt is the simplest type—just asking a question or giving an instruction without extra context or examples.

• Example: “Explain photosynthesis.”
• Strengths: Quick for broad overviews or background knowledge.
• Weaknesses: Vague, may miss nuance or tailor poorly to specific needs.
• Best use: Gathering general explanations or starting points when you don’t need depth.

8. Few-Shot Prompting

Few-shot prompting provides examples to guide the AI’s output style, tone, or structure.

• Example: Provide two short summaries of news articles, then ask the AI: “Using the same style, summarize this new article.”
• Strengths: Helps mimic a structure or voice, ensures consistency.
• Weaknesses: Still requires review for accuracy or alignment with purpose.
• Best use: Producing outputs that follow a set model, like summaries, lesson plans, or reports.

9. Role-Based Prompting

Role-based prompting tells the AI to “act” as a specific professional, persona, or viewpoint.

• Example: “You are a career coach. Draft advice for a recent college graduate preparing for their first job interview.”
• Strengths: Anchors terminology and assumptions in the right domain.
• Weaknesses: Role alone may not ensure depth; still needs context and constraints.
• Best use: Teaching, consulting, business writing, or scenario training.

Advanced Prompting Examples

Here is a useful video overview of advanced prompting techniques, including chain-of-thought, chaining, and refinement strategies:

Common Trade-Offs & Best Practices

• Model size matters: Larger models often benefit more from advanced prompting than smaller ones.
• Cost & latency: Producing multiple reasoning paths or long chains of thought can be slower or more expensive.
• Risk of hallucination: Even with advanced prompts, AI can hallucinate. Verification is still essential.
• Balance detail and flexibility: Overly rigid prompts can box the AI in; overly vague prompts lead to guesswork.

Analogy (click to expand)

📚 Weekly Reflection Journal

Choosing the Right Advanced Prompting Strategy

Looking Ahead

In the next chapter, we’ll examine the pitfalls of prompting, the common mistakes and misconceptions that can undermine your interactions with AI. From overly vague wording to unintentionally biased phrasing, we’ll highlight the traps to avoid and show you how to design prompts that are both effective and responsible.