Layered Context Prompting
What it does
Layer-by-layer enrichment of the prompt with context with clear division into logical blocks: TASK + FORMAT + DATA. Start with a simple request, then add quality context in blocks. Key principle: the signal matters, not the volume of information. The method allows you to gradually improve the result by adding examples and relevant context.
When it helps
Use this technique when you need to extract structured information from a large volume of data or generate content based on complex context. Especially effective when working with educational content, creating study materials, structuring knowledge. Apply when a simple prompt gives poor results, but there is an opportunity to add quality context. Ideal for situations where accuracy and relevance of results matter, not just the volume of information. The method helps
A prompt you can paste
Generic by design: it applies the technique without knowing your task. Adapt the marked parts.
# ЗАДАЧА Твоя задача — сгенерировать и отобразить ключевые концепции для учебного курса, а также их взаимосвязи. # ФОРМАТ ВЫВОДА Представь результат в виде структурированного списка. Для каждой концепции укажи: - **Концепция:** [Название концепции] - **Краткое описание:** [1-2 предложения, объясняющие суть концепции] - **Связанные концепции:** [Список других концепций из этого курса, с которыми эта концепция напрямую связана, через запятую] - **Тип связи:** [Опиши характер связи с каждой связанной концепцией (например, "предшествует", "является примером", "противоположность", "расширяет")] # ДАННЫЕ ## Название курса: [ВСТАВЬТЕ НАЗВАНИЕ КУРСА ЗДЕСЬ] ## Описание курса: [ВСТАВЬТЕ КРАТКОЕ ОПИСАНИЕ ИЛИ АННОТАЦИЮ КУРСА ЗДЕСЬ] ## Дополнительный контекст (опционально): [ВСТАВЬТЕ ЛЮБУЮ ДОПОЛНИТЕЛЬНУЮ ИНФОРМАЦИЮ, КОТОРАЯ ПОМОЖЕТ МОДЕЛИ ЛУЧШЕ ПОНЯТЬ КОНТЕКСТ КУРСА, например: - Целевая аудитория - Основные цели обучения - Список ключевых тем или модулей] ## Пример (для лучшего понимания формата): - **Концепция:** Основы Python - **Краткое описание:** Введение в синтаксис, переменные и базовые структуры данных языка Python. - **Связанные концепции:** Типы данных Python, Управляющие конструкции Python - **Тип связи:** предшествует, является основой для --- **Инструкция:** Сгенерируй не менее [УКАЖИТЕ КОЛИЧЕСТВО] концепций. Убедись, что взаимосвязи между концепциями логичны и отражают структуру учебного курса.
If this one does not fit, the two closest alternatives in the corpus are Multi-Aspect Cueing (MAC) and Structured Prompting with Negative Constraints, which target the same failure from a different angle.
Worked example
The same technique applied to a concrete job: pull structured fields out of inconsistent invoices. Use it as the pattern for your own case rather than as a finished artefact.
# Layered Context Prompting for Invoice Data Extraction ## Role You are an expert data extraction specialist with a keen eye for detail and a deep understanding of financial document structures. ## Task Description Your primary task is to extract specific, structured fields from inconsistent invoice documents. You need to identify and isolate key information such as Invoice Number, Invoice Date, Due Date, Vendor Name, Total Amount, and Tax Amount. The invoices may have variations in formatting, layout, and terminology. ## Format Indicator Present the extracted data in a JSON format. Each invoice should be an object within a main JSON array. For each invoice object, include the following keys: - `invoice_number` (string) - `invoice_date` (string, YYYY-MM-DD format) - `due_date` (string, YYYY-MM-DD format, if available) - `vendor_name` (string) - `total_amount` (float) - `tax_amount` (float, if available) If a specific field cannot be found, use `null` as its value. ## Information Injection **Context:** The invoices are from various vendors and may not follow a standard template. Some may use terms like "Payment Due By" instead of "Due Date", or "VAT" instead of "Tax". The goal is to create a clean, structured dataset from these varied inputs. **Key Fields to Extract:** - **Invoice Number:** Look for labels like "Invoice #", "Invoice No.", "Inv. #", or simply a prominent alphanumeric string often near the top. - **Invoice Date:** Search for labels like "Date", "Invoice Date", "Issue Date". Standardize to YYYY-MM-DD. - **Due Date:** Look for "Due Date", "Payment Due By", "Terms". Standardize to YYYY-MM-DD. This may not always be present. - **Vendor Name:** Identify the company issuing the invoice, often at the top, with an address. - **Total Amount:** Find the final amount payable, usually labeled "Total", "Grand Total", "Amount Due". This should be a numerical value. - **Tax Amount:** Look for "Tax", "VAT", "GST", "Sales Tax". This should be a numerical value. **Example of desired JSON output for a single invoice:**
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