token_1847 embedding attention logit_0.94 context retrieve generate safety output trace_id
LLM Use Case

Make LLM Inference Explainable

Every token generation, every context retrieval, every safety filter — observable, queryable, and auditable. Finally answer: "Why did the model say that?"

SD.31.08 • Large Language Model Observability

Live Event Stream — LLM Inference Engine

Tokens Processed

2.4M
per second

Avg Latency

47
milliseconds

Safety Filters

99.7%
pass rate

Hallucinations Caught

847
this hour

Inference Pipeline — Fully Observable

Prompt

User input received

llm.prompt.received:1

Retrieval

Context fetched

llm.context.retrieved:1

Inference

Tokens generated

llm.inference.completed:1

Safety

Filters applied

llm.safety.checked:1

Output

Response delivered

llm.response.sent:1

Query Any LLM Decision

Why did the model say that?

Trace the exact reasoning path for any response

SELECT context, attention_weights
FROM events
WHERE event_id = 'llm.inference.completed:1'
AND conversation_id = 'conv_abc123'

Was this a hallucination?

Check if the response was flagged for factual issues

SELECT confidence, sources
FROM events
WHERE event_id LIKE 'llm.hallucination.%'
AND response_id = 'resp_xyz789'

What context was used?

Review all retrieved documents that informed the response

SELECT documents, relevance_scores
FROM events
WHERE event_id = 'llm.context.retrieved:1'
AND trace_id = 'trace_456'

Why was this filtered?

Understand exactly why safety filters were triggered

SELECT filter_type, reason, score
FROM events
WHERE event_id = 'llm.safety.filtered:1'
AND request_id = 'req_def456'

Before vs. After Event Model

Before: Black Box LLM

Day 1 User reports inaccurate response
Day 3 Support escalates to ML team
Day 7 "We can't reproduce it"
Day 14 Regulator asks for explanation
Day 30 EU AI Act fine: millions (high-risk system)

After: Observable LLM

T+0 llm.inference.completed:1 — full trace
T+1ms llm.hallucination.detected:1 — flagged
T+5ms Response amended with correction
T+1hr Pattern analyzed, model updated
Audit Full inference chain in seconds

Why Observable LLMs Win

Hallucination Detection

Catch factual errors in real-time. Every claim traced to source documents or flagged as unsupported.

Training Data Provenance

Know exactly what data influenced each response. Critical for IP compliance and bias detection.

Safety Filter Transparency

Understand exactly why content was filtered. Tune policies with confidence, not guesswork.

Context Window Visibility

See exactly what context the model used. Debug retrieval issues and optimize prompts.

Attention Analysis

Understand which parts of the input drove the output. Explain model reasoning to stakeholders.

Regulatory Reporting

Generate EU AI Act transparency reports automatically. Every decision backed by evidence.

Built for High-Risk AI Compliance

EU AI Act
High-Risk System Requirements
NIST
AI Risk Management Framework
SOC 2
AI Model Governance
ISO 42001
AI Management System

Ready to Make Your LLM Explainable?

Join AI labs building trustworthy models through observable inference. Start with the Event Model today.