Structured Data Prompting
What it does
A method of formatting structured data in a prompt based on the principle of information locality. Related facts are grouped into single blocks (JSON, YAML structures), which allows the LLM to analyze data more effectively and find connections between objects, increasing answer accuracy 2-3 times compared to simple lists.
When it helps
Use this technique when passing structured data into the prompt (lists of facts, tables, knowledge graphs, databases) and their analysis, aggregation, or finding connections is required. Particularly effective for tasks with multiple objects and their attributes: analyzing employees and projects, processing product catalogs, working with hierarchical data. Apply when the model gives inaccurate answers about data or misses connections between facts. Critical for RAG systems and knowledge graph work.
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
You are an AI assistant designed to process and analyze structured data. Your task is to answer questions based **exclusively** on the data provided in the prompt. ### Context: Structured Data Please provide the data you want me to analyze. Format it using **JSON** or **YAML**. **Example for JSON:**
If this one does not fit, the two closest alternatives in the corpus are Time-Constrained Reasoning (Coarse-to-Fine & Answer-and-Verify) and Blueprint Prompting, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: turn a messy sales export into a defensible summary. Use it as the pattern for your own case rather than as a finished artefact.
You are an expert data analyst specializing in transforming raw, unstructured data into actionable insights using structured formats.
### Task: Transform Messy Sales Export into a Defensible Summary
### Context:
You will be provided with raw sales data that is likely in a disorganized or "messy" format. Your goal is to process this data, structure it logically, and then generate a concise, defensible summary that highlights key trends and insights.
### Technique: Structured Data Prompting (JSON/YAML)
Based on the "KG LLM Bench" research, presenting data in structured formats like JSON or YAML significantly improves LLM's ability to analyze and synthesize information. This is because related data points are grouped together, making them easier for the model to process.
### Data Input Format:
The raw sales data will be provided as a block of text. It might contain inconsistencies, missing values, or varied formatting.
### Action Plan:
1. **Data Structuring:**
* Identify the key entities within the sales data (e.g., "Sale", "Customer", "Product", "Date", "Amount", "Region").
* For each entity, identify all associated properties and values.
* Organize this information into a structured format. **Prioritize JSON** for its widespread compatibility and readability, but YAML is an acceptable alternative if JSON proves too complex for the given data. Ensure all properties for a single entity are grouped together in one block.
* Handle inconsistencies: Standardize date formats, currency symbols, and categorical labels where possible. Note any unresolvable ambiguities.
2. **Data Analysis & Summary Generation:**
* Once the data is structured, analyze it to identify key trends, patterns, and anomalies. Consider metrics such as:
* Total sales volume and value.
* Sales performance by region, product, or time period.
* Top-selling products.
* Customer segmentation insights (if data permits).
* Any significant outliers or unusual patterns.
* Generate a concise summary (approximately 3-5 bullet points) that highlights the most critical findings.
* The summary must be "defensible," meaning it should be directly supported by the structured data.
### Output Requirements:
* **Structured Data Block:** Present the cleaned and structured data first, clearly labeled (e.g., `### Structured Sales Data (JSON)`).
* **Summary:** Present the summary points after the structured data, clearly labeled (e.g., `### Sales Summary`).
* **Defensibility:** Ensure all claims in the summary can be traced back to specific data points within the structured block.
* **Format:** Use Markdown for clear sectioning (`##`, `###`).
---
### Input Data:
[Insert messy sales export text here]
---
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