have you tried putting a timer on the calls with a retry mechanic and prefiltering the data before the call? in addition - have you thought of the format u want? alot of people here do this -
how big of a chain are you looking for? you said tool + api so like a single prompt tool? agent reliant tool theres alot u can do brother
this picture is just the backend of such tools that do what you described, in your case code data
”{“professor”: “python”, “output”: “## Development Insight:\n\nThis blueprint outlines a comprehensive framework for integrating AI-driven cybersecurity measures specifically tailored for IPO companies. Here are some insights and potential improvements:\n\n1. **Modular Design**: The design is modular, which allows for easy updates and enhancements to individual components without affecting the entire system. This is a best practice in software architecture, promoting maintainability.\n\n2. **Real-time Data Processing**: Emphasizing real-time data feeds for monitoring and analysis is crucial, especially in cybersecurity, where threats can evolve rapidly. This can be implemented using event-driven architectures or streaming data platforms.\n\n3. **Feedback Loops**: The inclusion of feedback mechanisms, particularly in the Sentiment Analysis Module, will help to continuously improve the trust metrics based on real investor sentiments. This can also be extended to other components, creating a more adaptive system.\n\n4. **Visualization Tools**: The use of data visualization tools for real-time dashboards is essential for stakeholder engagement. It would be beneficial to consider user experience (UX) principles to ensure these dashboards are intuitive and actionable.\n\n5. **Fallback Mechanisms**: Redundancy and manual override features are critical for disaster recovery and maintaining operational integrity. Regular audits should also include automated processes to streamline compliance verification.\n\n6. **Integration Challenges**: Given the potential challenges of integrating AI systems with existing protocols, a phased approach to deployment might mitigate risks. Starting with pilot projects can help validate the effectiveness of the AI-driven measures.\n\n## Generated Code:\n\nThe following Python code demonstrates a simplified version of the AI Layer’s Sentiment Analysis Module and Regulatory Compliance Monitor. This example assumes access to investor feedback and regulatory changes for processing.\n\n```python\nimport random\nfrom datetime import datetime, timedelta\n\nclass SentimentAnalysisModule:\n def _init_(self):\n self.trust_metrics = \n\n def analyze_sentiment(self, feedback):\n # Simulate sentiment analysis\n sentiment_score = random.uniform(-1, 1) # -1 to 1 scale\n self.trust_metrics.append((datetime.now(), sentiment_score))\n return sentiment_score\n\n def generate_report(self):\n report = {\n \“average_trust\”: sum(score for _, score in self.trust_metrics) / len(self.trust_metrics),\n \“total_feedback\”: len(self.trust_metrics)\n }\n return report\n\n\nclass RegulatoryComplianceMonitor:\n def _init_(self):\n self.compliance_protocols = \n\n def update_protocols(self, regulation_change):\n # Simulate updating protocols based on new regulations\n self.compliance_protocols.append((datetime.now(), regulation_change))\n return f\“Updated protocols for: {regulation_change}\”\n\n def compliance_alerts(self):\n alerts = [f\“Change detected: {change[1]} on {change[0]}\” for change in self.compliance_protocols]\n return alerts\n\n\n# Example usage\nif _name_ == \”_main_\“:\n sentiment_module = SentimentAnalysisModule()\n compliance_monitor = RegulatoryComplianceMonitor()\n\n # Simulate investor feedback\n feedback_samples = [\“Great performance!\”, \“Needs improvement.\”, \“Trust issues with security.\”]\n\n for feedback in feedback_samples:\n sentiment_score = sentiment_module.analyze_sentiment(feedback)\n print(f\“Feedback: ‘{feedback}’ => Sentiment Score: {sentiment_score:.2f}\”)\n\n report = sentiment_module.generate_report()\n print(\“Sentiment Report:\”, report)\n\n # Simulate regulatory changes\n regulatory_changes = [\“GDPR updates\”, \“New data protection laws\”]\n for change in regulatory_changes:\n alert = compliance_monitor.update_protocols(change)\n print(alert)\n\n alerts = compliance_monitor.compliance_alerts()\n print(\“Compliance Alerts:\”, alerts)\n```\n\nThis code includes two classes: `SentimentAnalysisModule` and `RegulatoryComplianceMonitor`. The former simulates sentiment analysis based on investor feedback, while the latter tracks regulatory changes and updates compliance protocols. The example usage at the end demonstrates how to use these modules in practice.”, “session_id”: “ccore_cli_20250812083400”, “category”: “ipo_preparation”, “timestamp”: “2025-08-12T13:44:41.832094+00:00”}”
this can then be sent to a clean UI app, webhook and displayed on a website - a discord channel, whatever you want. this is easily obtained just by making a agent
heres most of a basic agent, basic meaning its just for data retrival which is what you described a small tool to give helpful data about coding - with a single key this agent can farm thousands of facts, using just the 4o-mini model at about 100-200 tokens per call. however - if you build a local storage for it, in something as generic as jsonl ( recommend pkl or npy) then u can reprocess that data locally and refine the data sent in the prompt automatically
if any of this doesnt make sense, just copy paste it into GPT and it can reverse engineer or elaborate on anything, also add to the agent, u can add rate limiting, key rotation, forking, payloading, multi agent, or use this agent in orchestration - within it youll see “professor” base, following this style allows you to expand or tweak your tool without much code editing by templating agent builds or task.
