Prompt Engineering

The most important prompt engineering techniques

• Set clear context — the role of the model, the goal of the request, the target audience.
• Few-shot prompting — supply examples of the desired input and output.
• Chain-of-thought (CoT) — ask the model to think step by step rather than answer straight away.
• Define the output format — JSON schema, bullet points, table, Markdown.
• Negative instructions — what should be avoided? (What NOT to do is often more effective than only stating what to do.)
• System prompts — the persistent role definition for the entire conversation.
• Self-refinement — let the model check and improve its own answer.

Prompt engineering in the SEO/GEO workflow

Four practical areas of use: (1) Generating content briefs — well-formulated prompts produce detailed H2/H3 structures that match search intent. (2) Title and meta optimisation — have the LLM generate 5–10 variants per page, with clear CTR levers stated in the prompt. (3) Auto-generating Schema.org markup — Article, FAQPage and HowTo markup based on the existing content. (4) Competitor analysis evaluations — structured summaries drawn from large volumes of data.

The limits of prompt engineering

What prompt engineering does not solve: factual truth (the risk of hallucination remains), genuine research (LLMs without RAG only know material up to the training cut-off), and deep expertise (an LLM can replace an expert, but cannot be one). What prompt engineering can achieve is: structural clarity in the answer, consistent output formats, reduced output variability, and targeted use of the model's strengths.