Temporal Graph Prompt Engineering Framework -Creating Threads of Time in Language Models

Time's Secrets: The Temporal Knowledge Graph Prompt Engineering (TKGP) framework empowers language models to analyze time-dependent data in legal, medical, financial, and historical domains, uncovering hidden connections and generating deeper insights.

Temporal Graph Prompt Engineering Framework -Creating Threads of Time in Language Models

The Temporal Knowledge Graph Prompt Engineering (TKGP) framework allows language models to navigate through information by understanding the temporal connections between concepts and events. It utilizes a knowledge graph visualization, making it easier to understand how the framework empowers language models to analyze time-dependent data in various domains.

TKGP is particularly useful in time-sensitive areas where understanding the interplay of events and concepts across specific timeframes is crucial. Examples include:

  • Legal Domain: Understanding the impact of legal clauses over time, identifying potential conflicts or risks based on their temporal interaction.
  • Medicine: Analyzing patient records to predict potential complications, optimizing treatment strategies by considering the temporal evolution of symptoms and medication effects.
  • Finance: Forecasting market trends based on historical data, identifying patterns in economic behavior over time.
  • History: Comprehending the events leading to significant historical turning points, delving into the nuances of causal relationships and contextual influences.

TKGP empowers language models to:

  • Identify Temporal Dependencies: Recognize how events, concepts, and actions relate to specific timeframes.
  • Discover Hidden Connections: Connect seemingly unrelated concepts based on their temporal proximity, context or shared influence.
  • Prioritize Relevant Information: Focus on the most impactful events or concepts based on their position within the timeline, leveraging the knowledge graph's hierarchical relationships.
  • Summarize Complex Information: Combine diverse knowledge from across the graph to generate concise, insightful interpretations, reflecting the dynamic interactions within the temporal context.

This framework provides a powerful tool for enhancing prompt engineering in time-related domains, allowing language models to perform more nuanced and sophisticated analyses, unlocking a deeper understanding of the intricacies of temporal events.

Framework for Dealing with Time-Based Challenges

This framework focuses on enhancing prompts for tasks involving temporal data.

Key Ideas:

  • Connect the Dots: Encourage models to consider relationships between events across time, even if seemingly disconnected at first glance. This resembles how expander graphs connect distant nodes in temporal graphs.
  • Prioritize Crucial Information: Guide the model to focus on the most relevant information rather than overwhelming it with data.
  • Avoid Information Loss: Structure prompts to prevent "oversquashing" of information – the equivalent of information bottlenecks.

Framework Components:

  1. Temporal Contextualization:
    • Explicit Time References: Provide clear time markers for events, enabling the model to understand relationships across different points in time.
    • Event Ordering: Specify the sequence of events, particularly when the order influences the outcome.
    • Time Intervals: Highlight intervals between events to help the model infer temporal dependencies.
  2. Expander Graph Analogy:
    • Highlight Connections: Use phrasing that emphasizes connections between seemingly unrelated events or entities. Examples: "events that are indirectly connected through...", "nodes sharing a common property," etc.
    • Leverage Contextual Clues: Embed clues in the prompt to guide the model towards "discovering" hidden connections. Examples: "based on their shared history," "due to potential interactions with..." etc.
  3. Focus & Prioritization:
    • Selective Emphasis: Highlight specific events or entities deemed crucial for achieving the desired outcome.
    • Filtering Mechanisms: Use prompts to guide the model to select relevant information. Examples: "the most influential events," "nodes that share a common attribute," etc.
  4. Information Flow Control:
    • Chunking: Break down complex prompts into smaller, digestible parts to prevent "oversquashing."
    • Structured Prompts: Use clear formatting and organization to enhance information flow and comprehension.
    • Intermediate Steps: Introduce prompts that guide the model through intermediate steps to gradually build understanding.

Example Prompt Adaptations:

  • Original Prompt: "Summarize the events in this timeline."
  • Enhanced Prompt (Temporal Contextualization): "Summarize the events in this timeline, focusing on the interactions between events that occurred within a week of each other."
  • Enhanced Prompt (Expander): "Analyze these events, identifying connections between seemingly unrelated occurrences, based on their shared context or potential influence on each other."
  • Enhanced Prompt (Focus & Prioritization): "Identify the three events that have the most significant impact on the overall narrative in this timeline."

Future Directions:

  • Fine-tuning Expander Graph Applications: Explore techniques to directly integrate expander graph concepts within prompt engineering. This could involve constructing prompt structures that mimic graph properties or using external expander graphs to guide information flow.
  • Beyond Temporal Graphs: Extend this framework to other domains where information flow and connections across data points are crucial, including text generation, knowledge retrieval, and decision-making systems.

Knowledge Graph Analogy

The framework can be easily understood through the lens of a knowledge graph. This analogy helps visualize how the framework encourages richer and more nuanced prompt interactions with language models.

When explaining these concepts I find it useful to abstract away the technical layers and imagine the language model’s knowledge base as a vast knowledge graph.

This abstraction helps visualize how prompt engineering influences the model's behavior, making it more intuitive to understand how to craft effective prompts for specific use cases.

In this graph:

  • Entities: Each word or concept is a node in the graph.
  • Relationships: The connections between these nodes represent how words or concepts are related, with probabilities indicating the strength or likelihood of these connections.

Entities as Nodes:

  • Each word or concept in a prompt becomes a node in the knowledge graph. For example: "event," "time," "connection," "influence" are all nodes.
  • This allows the framework to represent the prompt's vocabulary in a structured way.

Relationships as Connections:

  • Connections between nodes represent the relationships between words or concepts.
  • The strength of these connections is represented by probabilities, indicating the likelihood that a certain relationship exists.
  • For example, the relationship "EVENT IS CONNECTED TO TIME" might have a high probability, while "EVENT IS CONNECTED TO TEMPERATURE" might have a much lower probability.

Temporal Graph Prompt Engineering Framework:

  • Inducing connections between seemingly disconnected nodes, strengthening the network and enhancing information flow.
  • This involves creating "shortcuts" in the knowledge graph, particularly between temporally related entities.
  • For example, creating a strong connection between "EVENT 1" and "EVENT 2" even if they seem unrelated in the initial prompt, but occur close in time within the context.

Prompt Engineering Components in the Knowledge Graph:

  1. Temporal Contextualization:
    • Time Nodes: The knowledge graph features explicit nodes representing specific times (e.g., "2023-06-05").
    • Time-Based Relationships: Strong connections exist between events and their corresponding time nodes, ensuring the model understands the temporal context.
  2. Expander Graph Analogy:
    • Hidden Connections: The knowledge graph includes connections between entities with indirect relationships, as discovered through temporal analysis or contextual clues.
    • Relationship Probabilities: Stronger connections (higher probabilities) between related events, even if seemingly unconnected initially, help guide the model to understand wider connections.
  3. Focus & Prioritization:
    • Node Importance: Certain nodes are designated as "important" based on the prompt's objectives.
    • Hierarchical Connections: The knowledge graph may feature hierarchical relationships reflecting the importance of certain nodes and their influence on the overall understanding.
  4. Information Flow Control:
    • Graph Traversal: The language model is guided to traverse the knowledge graph efficiently, finding paths between important nodes and navigating through connections with the highest probabilities.
    • Chunking: Breaking down complex events, data and prompts conceptually attempts to organize the knowledge graph into smaller, manageable subgraphs for easier traversal.

Benefits of the Knowledge Graph Analogy:

  • Clear Visualization: The knowledge graph provides a clear and intuitive representation of the prompt's structure and relationships.
  • Enhanced Understanding: Mapping prompts onto a knowledge graph allows for a deeper understanding of the information flow and connections within the prompt.
  • Improved Prompt Design: This helps create more effective prompts by consciously building a richer, more connected knowledge graph, thus facilitating better model responses.

Temporal Graph Prompt Engineering Framework

Understanding Contract Clauses with Temporal Knowledge Graphs

This example showcases how the framework can be applied to enhance legal analysis by understanding complex contractual clauses. The same method can be used in other domains such as Medicine or finance. The key is to ensure your data is temporally labelled as much as possible.


A law firm is reviewing a 5-year contract for a software development project. They need to identify potential legal risks based on the interplay of various clauses and their evolution over time.

Knowledge Graph Setup:

  • Nodes: Each word, term, or concept from the contract becomes a node. Examples: "termination," "payment," "performance," "breach," "notice period," "liability," "force majeure," "year 1," "year 2," etc.
  • Relationships: Connections between nodes represent legal relationships, logical deductions, or temporal dependencies. Example: "BREACH IS RELATED TO TERMINATION," "PAYMENT IS DEPENDENT ON PERFORMANCE," "YEAR 1 IS BEFORE YEAR 2," "FORCE MAJEURE MAY AFFECT PERFORMANCE," etc.
  • Probabilities: The strength of these connections is determined by legal expertise and varies based on the specific contractual context. "BREACH IS RELATED TO TERMINATION" will have a stronger connection than "PAYMENT IS RELATED TO FORCE MAJEURE."

Prompt Engineering Steps:

  1. Temporal Contextualization:
    • Prompt: "Analyze the contract clauses. Provide a timeline of each clause pertaining to termination, payment, and performance, highlighting the dates or time periods they apply to."
    • Result: The model generates a timeline for each clause, annotating their applicability based on specified timeframes. This process creates strong connections in the knowledge graph between clauses and their temporal context.
  2. Expander Graph Analogy:
    • Prompt: "Given the timeline, identify any potential legal risks based on the interactions between clauses across different time periods. For example, if a performance clause changes in year 3, how does it impact termination provisions or payment obligations?"
    • Result: The model explores connections between seemingly unrelated clauses based on their temporal proximity or potential influence. This introduces new relationships (with varying probabilities) between nodes in the knowledge graph, strengthening the network.
  3. Focus & Prioritization:
    • Prompt: "Focusing on the most critical clauses regarding liability and force majeure, analyze how their interaction over time might impact the client's risk exposure."
    • Result: The model prioritizes certain nodes for in-depth analysis, based on their potential risk factors. This highlights and strengthens connections relevant to those key issues, guiding the model to focus on the most impactful information.
  4. Information Flow Control:
    • Prompt: " Summarize the identified legal risks in bullet points, indicating the clauses involved, the specific time periods they relate to, and the potential consequences for the client."
    • Result: The model provides a cohesive summary, demonstrating its ability to navigate the complex knowledge graph, synthesizing information across different nodes and relationships.

ChatGPT Prompts for each Step:

Information Flow Control:

Summarize the identified legal risks in bullet points, indicating the clauses involved, the specific time periods they relate to, and the potential consequences for the client. 

Focus & Prioritization:

Focusing on the most critical clauses regarding liability and force majeure, analyze how their interaction over time might impact the client's risk exposure.

Expander Graph Analogy:

Given the timeline you provided, identify any potential legal risks based on the interactions between clauses across different time periods.  For example, if a performance clause changes in year 3, how does it impact termination provisions or payment obligations? 

Temporal Contextualization:

Analyze the following contract clauses: [Insert relevant contract clauses here]. Provide a timeline of each clause pertaining to termination, payment, and performance, highlighting the dates or time periods they apply to.  


This approach, using temporal knowledge graphs enhanced by "Temporal Graph Rewiring," allows for:

  • Comprehensive Legal Analysis: The model explores complex relationships between clauses across time, going beyond superficial analysis.
  • Risk Identification: Potential legal risks are surfaced based on the temporal interaction of clauses.
  • Informed Decision-Making: The law firm gains a deeper understanding of potential legal issues by leveraging the model's ability to navigate the temporal knowledge graph.

By integrating this approach into their workflow, law firms can improve their legal analysis and risk identification.

Temporal Knowledge Graph Prompt Engineering (TKGP) Workflow: Detailed Prompt Templates

This document provides generalized and detailed prompt templates for each step of the TKGP workflow, adaptable to diverse time-related domains. The aim is to provide a relevant starting point for any endeavour.

Remember: These templates are starting points. Customize them based on your specific domain, data, and desired output.

Step 1: Temporal Contextualization

Goal: Extract temporal information from the input, establishing initial connections between events and timeframes.

Prompt Template:

Analyze the following data: [Input data].  

Provide a timeline summarizing the key events/concepts/actions, highlighting their specific dates, times, or time periods. Consider **[key time-related keywords specific to your domain]** for a comprehensive analysis. 

For example, in a medical context: 
- "Provide a timeline of the patient's symptoms, noting their onset, duration, and progression."

Output your response in a tabular format with columns for Time, Event/Concept, and Additional Information (as relevant).

Step 2: Expander Graph Analogy

Goal: Identify potential relationships between temporally connected events/concepts, expanding the knowledge graph's connections.

Prompt Template:

Given the timeline you generated, explore potential relationships between events/concepts based on their temporal proximity, context, or influence on each other.  

For example: 
- "In a legal context: Explore how a change in a contract clause in year 3 might affect earlier or later provisions."
- "In a medical context: Analyze whether a patient's symptoms in week 1 might influence their treatment plan in week 3."

Use this format: "EVENT/CONCEPT A [temporal connection] EVENT/CONCEPT B [potential impact or relationship]."

Step 3: Focus & Prioritization

Goal: Direct the model towards specific areas of interest, highlighting key nodes in the knowledge graph.

Prompt Template:

Focusing on  [key events/concepts/actions] related to **[domain-specific goals or objectives]**, analyze how their interactions over time might impact **[desired outcome or analysis objective]**.  

For example:
- "In a financial context: Analyze how changes in interest rates in 2023 might impact long-term investment strategies." 
- "In a legal context: Analyze the potential consequences of a specific clause's implementation in the  5th year of a contract."

Specifically focus on **[domain-specific keywords]** for a deeper analysis. 

Step 4: Information Flow Control

Goal: Summarize the model's findings, synthesizing information across the knowledge graph.

Prompt Template:

Based on your previous analyses:

Summarize the key insights and findings related to **[domain-specific goals or objectives]**, focusing on the interplay between temporal events/concepts. 

Provide a concise summary as bullet points addressing:
- **[Key aspects relevant to your domain]:** (e.g., potential risks, recommended actions, key trends, etc.)
- **[Temporal context:** (e.g., time periods, specific dates, duration of impact).
- **[Relationships or connections]:** (e.g.,  "Event A impacts Event B" or "Concept C is dependent on Concept D").

Output a structured and informative summary using a clear and concise style. 

Example Application:

Let's consider analyzing a contract in the legal field.

  • Step 1: Create a timeline of key clauses related to termination, payment, and performance.
  • Step 2: Explore how changes in the performance clause in year 3 might impact termination provisions or payment obligations in later years.
  • Step 3: Focus on liability clauses and how they interact with force majeure provisions over time.
  • Step 4: Summarize potential legal risks based on the temporal interactions between these clauses, indicating specific timeframes and potential consequences.

Key Considerations:

  • Domain Adaptability: Customize prompts to align with your specific area of expertise and the nature of the data.
  • Iteration and Refinement: Experiment with different prompt variations to optimize model performance for your domain.
  • Data Quality and Structure: Ensure your input data is well-organized and provides sufficient temporal context for effective knowledge graph construction.

TKGP, combined with these detailed prompt templates, offers a flexible framework for leveraging language models to analyze and understand complex time-related information across numerous domains.

The Crucial Role of Defining the AI Agent for TKGP

Before diving into the TKGP framework, clearly defining the AI agent is paramount for ensuring enhanced, targeted, and accurate responses. A misaligned agent can lead to irrelevant outputs, even with sophisticated prompts.

Here are key considerations for defining your AI agent:

1. Role:

  • Legal Expert: An AI agent focused on legal analysis might need access to legal databases, statutes, and case law.
  • Medical Advisor: A medical AI agent needs a vast knowledge of medical practices, diagnoses, and treatments.
  • Financial Analyst: A financial agent might require integration with financial data sources and knowledge of market trends.
  • Historian: An historical AI agent needs access to historical records, events, and cultural contexts.

2. Cognitive Skills Graph:

  • This details the specific cognitive skills required for the agent's role.
  • Examples:
    • Logic and Reasoning: Analyzing cause-and-effect relationships, identifying legal implications, or drawing inferences from medical evidence.
    • Information Retrieval: Accessing relevant knowledge from databases, documents, or other sources.
    • Time Perception: Understanding how time influences relationships between events and concepts.
    • Summarization and Synthesis: Creating concise and informative summaries from complex temporal data.

3. Skills Graph:

  • Language Proficiency: The agent should excel at natural language understanding and generation, adept at interpreting and conveying complex information.
  • Data Processing: Ability to handle and analyze large volumes of data, specifically those with temporal aspects.
  • Domain Expertise: Deep knowledge relevant to the chosen field.

4. Writing Style:

  • Formal vs. Conversational: The chosen writing style should align with the agent's role. For legal experts, highly formal language may be preferred, while medical advisors might need a clear and direct style.
  • Audience Considerations: Adapt the writing style based on the intended audience (e.g., lawyers, doctors, investors, historians).

5. Ethical Considerations:

  • Bias and Fairness: Train the agent to be unbiased and fair in its responses, ensuring it doesn't perpetuate or amplify existing prejudices.
  • Transparency and Explainability: The agent should be able to explain its reasoning and decision-making process, fostering trust and transparency.
  • Data Privacy: Protect user data and ensure compliance with privacy regulations.

Sometimes the most potent solutions lie in the simplicity of our approaches. As we go deeper into the complex world of AI, it's easy to get caught up in intricate frameworks and advanced techniques. Yet, sometimes the most effective strategies are the most basic.

This is the core idea behind the Temporal Knowledge Graph Prompt Engineering (TKGP) framework – to harness the power of simple, intuitive processes for better understanding of temporal data. Think of it as a way to "weave threads of time" by feeding our AI models data in chronological chunks, tagging information in time-based sequences. This approach, while seemingly basic, allows us to uncover insightful connections and patterns that might otherwise remain hidden.

Coupled with other prompt engineering techniques, we can create powerful workflows for analyzing temporal data in various domains. From legal contracts to patient records, financial reports to historical events, TKGP offers a flexible framework for unlocking the complexities of time-sensitive data.

Remember, sometimes the most elegant solutions are often the simplest. Let's explore the power of TKGP and unlock a more profound understanding of the temporal interactions that shape our world.

Join Our Prompt Engineering Community:

This community can serve as a platform for:

  • Sharing Expertise: Collaborating to define best practices for agent design and prompt engineering.
  • Testing Prompts: Experimenting with prompts and refining them based on community feedback.
  • Developing Tools and Resources: Creating tools and resources to assist in the development of AI agents and prompt engineering strategies.

By carefully defining the AI agent's role, cognitive skills, technical skills, writing style, and ethical considerations, you can build a foundation that ensures the TKGP framework delivers enhanced, targeted, and accurate results, tailored to specific domains and applications.

Let's continue the conversation in the TKGP Prompt Engineering Community!

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