ContextJet.ai
Official@contextjet-ai · India
ContextJet provides observability, evaluation, and security governance for production-grade generative systems through OpenTelemetry-based tracing and rigorous performance benchmarking.
Agent Skills by ContextJet.ai
Showing 26 vetted skills indexed across 1 GitHub repositories.
ContextJet-aitrace-multi-agent-system
Generate a nested span tree trace of multi-agent system interactions.
ContextJet-aibuild-eval-dataset
Create and verify layered evaluation datasets for LLM applications.
ContextJet-aivalidate-genai-spans
Validate LLM spans for OpenTelemetry GenAI specification compliance.
ContextJet-aichoose-observability-stack
Recommend LLM observability tools based on deployment, budget, and constraints.
ContextJet-aiannotate-traces-for-review
Annotate LLM traces for human review and error analysis.
ContextJet-aieval-driven-development
Write evaluations before changing LLM prompts or models.
ContextJet-aiadd-llm-guardrails
Add input and output validation, PII detection, and toxicity checks to LLM applications.
ContextJet-aiinstrument-llm-observability
Add OpenTelemetry tracing to LLM applications capturing prompts, tool calls, token usage, latency, and cost.
ContextJet-aicompare-llm-models
Compare LLMs using a custom evaluation suite for cost, latency, and reliability.
ContextJet-aicollect-user-feedback
Collect user feedback on LLM outputs and integrate it with observability tools.
ContextJet-aiset-up-drift-alerts
Set up drift alerts for LLM apps using OpenTelemetry GenAI semantic conventions.
ContextJet-airedact-pii-for-tracing
Redact personally identifiable information from LLM observability data before exporting to tracing backends.
ContextJet-aimonitor-rag-quality
Evaluate RAG pipelines using context precision, recall, faithfulness, and answer relevance.
ContextJet-aiset-up-ab-testing
Split live traffic between control and experimental groups to compare LLM prompt and model outcomes.
ContextJet-aidebug-agent-from-traces
Analyze observability traces to diagnose LLM agent issues.
ContextJet-aicheck-answer-consistency
Detect potential misinformation in LLM answers by measuring consistency across samples.
ContextJet-aiscrub-pii-from-text
Mask PII in text with a Python scrubber using Luhn validation.
ContextJet-aireduce-llm-cost
Analyze LLM observability data to identify and reduce cost inefficiencies.
ContextJet-aimeasure-agent-task-success
Measure end-to-end success rates and task paths of AI agents using tracing data.
ContextJet-aitrace-based-testing
Convert production traces into regression test cases for LLM applications.
ContextJet-aired-team-llm-app
Red-team LLM applications against security vulnerabilities like prompt injection and jailbreaks.
ContextJet-aiadd-llm-fallbacks
Implement retries, fallbacks, and monitoring for LLM applications.
ContextJet-aiadd-llm-evals
Add offline and online evaluation scoring to LLM and agent applications.
ContextJet-aioptimize-prompts
Select the best prompt variation based on performance metrics on an evaluation set.
Frequently Asked Questions About ContextJet.ai
FAQPage SchemaWhat specific tasks can engineers perform using these capabilities?▼
Engineers can instrument production environments to capture token usage, latency, and cost, while simultaneously implementing guardrails for PII redaction, toxicity filtering, and hallucination detection to ensure reliable output quality.
Which personas benefit most from these observability and evaluation features?▼
Machine learning engineers, reliability architects, and security practitioners benefit by gaining granular visibility into model behavior, enabling data-driven prompt optimization, and maintaining compliance through rigorous trace-based testing.
How are these observability and evaluation standards implemented in runtime?▼
Implementation relies on integrating OpenTelemetry semantic conventions into the application stack to emit structured spans, which are then processed for drift alerts, cost analysis, and performance benchmarking against custom evaluation suites.