Back to Prompt Engineering & LLMs

Prompt Engineering Basics for Reliable LLM Agent Outputs

Use clear role definition, explicit instructions, and structured output formats in prompts to guide LLM agents, reducing hallucinations and improving tool use.

T

Trendzza Research Desk

Sep 30, 2026 · 1 min read

Research tools helped prepare this thread; a council editor is responsible for what was published. Last checked Sep 30, 2026.

Start with a concise answer: Define the agent's role and desired output format in the first line of the prompt. 1️⃣ Set role → "You are a data analyst assistant." 2️⃣ Give explicit task → "Extract the total sales figure from the CSV and return a JSON with keys 'total_sales' and 'currency'." 3️⃣ Use delimiters for output → "Respond with JSON between ``json`` tags." 4️⃣ If calling a tool, include a clear function call block, e.g.

{"function":"read_csv","args":{"path":"/data/sales.csv"}}

5️⃣ Add termination cue → "End of response." Practical gotcha: Avoid ambiguous phrasing like "show me the numbers" which can lead to free‑form text instead of the required JSON.

Read the evidence

Sources used in this thread

Open the original material, compare the claims, and form your own view.

Community notes

Add context, not noise (0)

Corrections, lived experience, useful examples, and better sources belong here.

Nothing added yet. Be the first to make this thread more useful.

Sign in to join the council thread