## Overview This research paper from Technical University of Munich presents a comprehensive empirical study of prompt templates used in production LLM-powered applications (LLMapps). The study is particularly relevant to LLMOps practitioners because it provides data-driven insights into how real-world applications structure their prompts, derived from analyzing open-source repositories from major companies including Uber and Microsoft. The core motivation stems from a significant operational challenge: while LLMs have democratized AI adoption, crafting effective prompts remains non-trivial. Small variations in prompt structure or wording can lead to substantial differences in model output, creating reliability and maintainability challenges for production systems. Prompt templates serve as a solution by providing predefined structures that combine static text with dynamic placeholders, enabling consistent and efficient LLM interactions at scale. ## Data Collection and Methodology The researchers constructed their dataset from PromptSet, a collection of prompts extracted from LLMapps in open-source GitHub projects as of January 2024. Their data processing pipeline employed several quality filters that are instructive for LLMOps practitioners building similar analysis systems: - Started with 14,834 non-empty English prompts from the PromptSet dataset - Filtered repositories with at least five GitHub stars and recent updates within the past year, reducing to 2,888 records across 1,525 repositories - Separated multi-prompt records into individual entries (5,816 prompts) - Removed duplicates to obtain 4,540 unique prompts - Excluded prompts shorter than five tokens using spaCy tokenization - Extracted 2,163 distinct prompt templates using llama3-70b-8192 model The dataset includes production tools from notable organizations: Uber's Piranha (a tool for refactoring code related to feature flag APIs, adopted by over 200 developers), Microsoft's TaskWeaver (a code-first agent framework for data analytics with over 5k GitHub stars), Weaviate's Verba RAG chatbot (6.5k stars), and LAION-AI's Open-Assistant (37k stars). ## Component Analysis Findings The researchers identified seven common component types in prompt templates, derived from synthesizing insights from Google Cloud documentation, the Elavis Saravia framework, the CRISPE framework, and the LangGPT framework. The distribution of components provides insight into production template design: - **Directive** (86.7%): The core intent of the prompt, almost always present as it guides the LLM on task execution - **Context** (56.2%): Background information including input content and contextual descriptions - **Output Format/Style** (39.7%): Type, format, or style specifications for the output - **Constraints** (35.7%): Restrictions on what the model must adhere to when generating responses - **Profile/Role** (28.4%): Definition of who or what the model is acting as - **Workflow** (27.5%): Steps and processes the model should follow - **Examples** (19.9%): Few-shot examples demonstrating desired responses A key operational finding is the common sequential order of components: Profile/Role and Directive typically appear first (establishing model identity and task intent), while examples are typically placed at the end. The researchers found that over 90% of directives are written in instruction style rather than question style, suggesting that commands like "Summarize the report" are more effective for production systems than questions like "Could you summarize this?" ## JSON Output Format Patterns Given that JSON is the most commonly used structured output format in LLMapps (critical for downstream processing), the researchers identified three distinct patterns for specifying JSON output: - **Pattern 1** (36.21%): Only indicates that output should be in JSON format with general natural language guidelines - **Pattern 2** (19.83%): Specifies JSON format plus explicit attribute names - **Pattern 3** (43.97%): Specifies JSON format, attribute names, and detailed descriptions for each attribute Sample testing with both llama3-70b-8192 and gpt-4o revealed significant performance differences. Pattern 3 achieved the highest scores across both format-following and content-following metrics. For format-following specifically, the llama3 model scored 3.09 with Pattern 1 versus 4.90 with Pattern 3 (on a 1-5 scale), demonstrating that explicit attribute definitions dramatically improve structural consistency. The researchers also found that using JSON format definitions alone is insufficient to prevent extraneous explanations in output. Combining positive instructions (output format definitions) with negative instructions (exclusion constraints like "Do not provide any other output text beyond the JSON string") raised the format-following rate from 40% to 100% for llama3 and from 86.67% to 100% for gpt-4o. ## Placeholder Analysis The study identified four primary placeholder categories in prompt templates: - **Knowledge Input** (50.9%): The core content that the prompt directly processes (e.g., documents, code snippets) - **Metadata/Short Phrases** (43.4%): Brief inputs defining parameters like language or username - **User Question** (24.5%): Direct queries from end users - **Contextual Information** (19.5%): Background or supplementary input Regarding positional distribution, approximately 60% of user questions appear at the end of templates, while Knowledge Input placeholders are more evenly distributed between beginning and end positions. Testing revealed that positioning task intent instructions after the Knowledge Input (rather than before) significantly improves output quality, particularly for long inputs. The llama3 model showed a +0.91 improvement in task intent adherence with this positioning strategy, compared to +0.34 for gpt-4o. The researchers also flagged a common anti-pattern: many templates use non-semantic placeholder names like "text" (4.44%) and "input" (2.35%), which hinder maintainability. Similar to variable naming conventions in traditional software, clear and descriptive placeholder names are recommended for production systems. ## Constraint Patterns Analysis of constraint types revealed that exclusion constraints are most common (46.0%), followed by inclusion constraints (35.6%) and word count constraints (10.5%). The researchers further classified exclusion constraints into subcategories: - **Accuracy and relevance**: "Avoid adding any extraneous information" - **Clarity about unknowns**: "If you don't know the answer, just say that you don't know, don't try to make up an answer" - **Output control**: "Do not provide any other output text or explanation" - **Redundancy and context adherence**: "Don't give information outside the document or repeat your findings" - **Technical restriction**: "Never write any query other than a select" These exclusion constraints serve as guardrails against hallucination and help narrow the generation space, which are critical concerns for production LLM deployments. ## Implications for LLMOps Practitioners The study offers several actionable insights for production LLM systems: **For Template Maintenance**: Prompt templates should adapt dynamically based on user feedback and usage analytics. Analyzing historical input patterns (lengths, content types) helps optimize placeholder positions and component ordering to prevent information decay in long prompts. **For Model Selection and Cost Optimization**: Well-designed prompt templates can significantly strengthen weaker models' instruction-following abilities. In the long-input experiments, the output quality boost achieved with optimized templates for llama3-70b-8192 was nearly double that of gpt-4o. This suggests that developers should first consider redesigning prompt templates before switching to more expensive models. **For In-Context Learning Trade-offs**: Fewer than 20% of production applications use few-shot examples in their templates. The researchers suggest that in-context learning is not a one-size-fits-all solution, and clearly defined prompt templates can sometimes outperform few-shot approaches while avoiding increased token costs and potential semantic contamination. **For LLM API Providers**: The study recommends that providers offer pre-defined templates for common tasks and automated template evaluation tools that compare outputs across different template patterns and provide explainability for optimization recommendations. ## Validation Approach The researchers employed a rigorous validation methodology combining LLM-assisted analysis with human review. Component identification using llama3-70b-8192 achieved 86% precision at the component level and 66% full-match precision at the prompt level (99% partial match). Placeholder classification achieved 81% accuracy after an iterative refinement process. Human evaluators with programming experience independently reviewed LLM-generated classifications, with final scores being averages of their assessments. ## Limitations and Considerations While this study provides valuable empirical insights, several limitations should be noted. The dataset is derived from open-source repositories, which may not fully represent proprietary production systems. The quality filtering based on GitHub stars and recent updates, while reasonable, may exclude valid but less popular applications. Additionally, the testing was conducted on specific model versions (llama3-70b-8192 and gpt-4o as of the study period), and results may vary with newer model versions or different LLM families.