Troubleshooting & Diagnostics

GenAI Reliability &
Forensic Engineering

Production AI breaks in new and creative ways. We provide the Diagnostic Toolchain to identify and fix issues fast—from context overflows and API timeouts to subtle prompt drift and retrieval decay.
Content Handling

Context Optimization Studio

"Context Window Exceeded" is the enemy of production AI. We diagnose overflow issues and implement Dynamic Chunking and Recursive Summarization strategies to fit maximum signal into the available token budget.
Context Usage12500 / 8192 Tokens
OVERFLOW DETECTED: Truncation will occur.
Payload VisualizationStrategy: Concatenating all retrieved documents blindly.
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Live Diagnostics Console

System.log
Waiting for simulation...
Retry Strategy
Exponential Backoff
Error Handling
Graceful Fallback
Integration Issues

Resilient API Patterns

Cloud APIs are flaky. We troubleshoot and resolve integration failures by implementing Robust Client Wrappers. Our architecture handles throttling (HTTP 429), timeouts (504), and malformed responses automatically, ensuring your application recovers without user intervention.
  • Idempotency: Safe retries without duplicate actions.
  • Timeout Management: Strict bounds on inference time.
Prompt Debugging

Prompt Regression Testing

When model outputs degrade, is it the model or the prompt? We use Versioned Prompt Comparisons to troubleshoot quality issues. By systematically refining instructions and adding constraints, we resolve "Prompt Confusion" and hallucination issues.
Input Prompt
"Write a SQL query to find users."
Analysis
Too generic
Potential security risk (SELECT *)
No schema context
Model Output
SELECT * FROM users;

Retrieval Relevance Monitor

DRIFT DETECTED
Retrieval Troubleshooting

Diagnosing Drift

When RAG performance drops, it's often due to Embedding Drift (data distribution changes) or Stale Indices. We implement monitors that track the cosine similarity of top results over time. If relevance drops below a threshold, we trigger automated re-indexing or embedding model fine-tuning.

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