context is king

Additional Content: Context Analysis with ChatGPT by OpenAI

Context is King

As discussed in the Claude example, its lack of a sufficient user profile limited its ability to suggest actionable next steps. In contrast, the next step in the user journey is to check for additional context, which can result in more personalized and actionable guidance.

To explore this, I used ChatGPT by OpenAI, which stores memories if the user selects it. Over time, I’ve utilized ChatGPT to store context while learning about various programming languages, third-party services, and general business approaches. While UNPARTY takes a different approach to providing context, I was curious to see how ChatGPT would process the Claude session’s output when enriched by the context it already had about the UNPARTY project.

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ChatGPT’s Response

Using the output from the Claude session, ChatGPT produced actionable next steps for integrating these insights into the UNPARTY platform:

Next Steps for Building This in UNPARTY

1. Extract and Organize Context

   • Tools:

     • NLTokenizer to break passages into tokens and identify key phrases.

     • Named Entity Recognition (NER) to extract themes such as "fear," "quiet," or "nothing to say."

   • Output: A structured analysis of the user's writing, identifying problems and their relationships.

2. Model Problem-Action Relationships

   • AI Task: Build a map of relationships between extracted problems and potential solutions.

   • Techniques: Use sentiment analysis or clustering to group interconnected issues.

   • Example:

     • Core Problem: "Fear disrupts writing flow."

     • Actionable Insight: "Try writing explicitly for yourself, without thinking of an audience."

3. Suggest Actionable Solutions

   • AI Task: Provide real-world, confidence-building solutions, such as:

     • Journaling prompts.

     • Tools to create a quiet environment (e.g., noise-canceling app suggestions).

     • Reflection workflows.

4. Offer Visualizations

   • Why: Diagrams and workflows help users visualize their progress and roadblocks.

   • How: Use libraries like D3.js or Chart.js to render problem-solution relationships in-app.

5. Expand with User Interaction

   • Allow users to provide feedback on AI insights.

   • Use responses to refine problem identification and solution generation.

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Programming Languages and Frameworks

ChatGPT further suggested tools and technologies for implementation:

• Frontend:

  • SwiftUI for building the iOS app interface.

  • D3.js for dynamic visualizations beyond native iOS capabilities.

• Backend (AI Integration):

  • Python with frameworks like:

    • FastAPI for lightweight APIs connecting to AI services.

    • Hugging Face Transformers for fine-tuning AI models to identify relationships between themes.

  • Pinecone for embedding storage and relationship mapping.

• Data Pipeline:

  • Apple’s Core ML for fine-tuning on-device models for private NLP tasks.

  • Federated learning to improve the model without exposing user data.

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Key Insights

The most fascinating part of ChatGPT’s response was its ability to integrate prior knowledge from my ideation phase with new context. For example, tools like Swift, D3.js, and NLP frameworks were already familiar to me, as I had explored and documented them during earlier stages of building UNPARTY. This personalized approach highlights the power of AI to tailor actionable insights to the user’s existing expertise and preferences.

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Visualizing the Solution

To demonstrate the model’s ability to organize problem-solution relationships visually, I asked for a graph to represent its conclusions. Below is a Python-based implementation using matplotlib and networkx:

import matplotlib.pyplot as plt
import networkx as nx

# Define the problems and solutions as nodes
nodes = {
    "Disruption of Writing Flow": "Core Problem",
    "Fear-Based Self-Censorship": "Problem Source",
    "Environmental Barriers": "Problem Source",
    "Internal Mental Blocks": "Problem Source",
    "Write Without an Audience": "Solution",
    "Organize Quiet Space": "Solution",
    "Daily Reflection Prompts": "Solution",
}

# Define relationships between problems and solutions
edges = [
    ("Disruption of Writing Flow", "Fear-Based Self-Censorship"),
    ("Disruption of Writing Flow", "Environmental Barriers"),
    ("Disruption of Writing Flow", "Internal Mental Blocks"),
    ("Fear-Based Self-Censorship", "Write Without an Audience"),
    ("Environmental Barriers", "Organize Quiet Space"),
    ("Internal Mental Blocks", "Daily Reflection Prompts"),
]

# Create the graph
G = nx.DiGraph()
G.add_edges_from(edges)

# Define node colors and labels
node_colors = [
    "#ffcccb" if "Problem" in nodes[node] else "#ccffcc" for node in G.nodes()
]
node_labels = {node: node for node in G.nodes()}

# Plot the graph
plt.figure(figsize=(10, 8))
pos = nx.spring_layout(G, seed=42)
nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=3000)
nx.draw_networkx_edges(G, pos, arrows=True, arrowstyle="->", edge_color="gray")
nx.draw_networkx_labels(G, pos, labels=node_labels, font_size=10, font_weight="bold")
plt.title("UNPARTY Problem-to-Solution Mapping", fontsize=14, fontweight="bold")
plt.axis("off")
plt.show()

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Takeaway

The core problem—"Disruption of Writing Flow"—sits at the center, linked to its contributing sources:

• Fear-Based Self-Censorship → "Write Without an Audience"

• Environmental Barriers → "Organize Quiet Space"

• Internal Mental Blocks → "Daily Reflection Prompts"

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How This Exercise Fits into UNPARTY

This exercise highlights a foundational element of UNPARTY's approach to AI: bridging reflection and action through actionable insights. By integrating personalized suggestions, familiar tools, and actionable outputs like code, components, and connections, UNPARTY empowers users to confidently take their next steps with solutions that are both meaningful and practical.

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