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plugins/phoenix/skills/phoenix-evals/references/axial-coding.md

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+# Axial Coding
+
+Group open-ended notes into structured failure taxonomies.
+
+## Process
+
+1. **Gather** - Collect open coding notes
+2. **Pattern** - Group notes with common themes
+3. **Name** - Create actionable category names
+4. **Quantify** - Count failures per category
+
+## Example Taxonomy
+
+```yaml
+failure_taxonomy:
+  content_quality:
+    hallucination: [invented_facts, fictional_citations]
+    incompleteness: [partial_answer, missing_key_info]
+    inaccuracy: [wrong_numbers, wrong_dates]
+  
+  communication:
+    tone_mismatch: [too_casual, too_formal]
+    clarity: [ambiguous, jargon_heavy]
+  
+  context:
+    user_context: [ignored_preferences, misunderstood_intent]
+    retrieved_context: [ignored_documents, wrong_context]
+  
+  safety:
+    missing_disclaimers: [legal, medical, financial]
+```
+
+## Add Annotation (Python)
+
+```python
+from phoenix.client import Client
+
+client = Client()
+client.spans.add_span_annotation(
+    span_id="abc123",
+    annotation_name="failure_category",
+    label="hallucination",
+    explanation="invented a feature that doesn't exist",
+    annotator_kind="HUMAN",
+    sync=True,
+)
+```
+
+## Add Annotation (TypeScript)
+
+```typescript
+import { addSpanAnnotation } from "@arizeai/phoenix-client/spans";
+
+await addSpanAnnotation({
+  spanAnnotation: {
+    spanId: "abc123",
+    name: "failure_category",
+    label: "hallucination",
+    explanation: "invented a feature that doesn't exist",
+    annotatorKind: "HUMAN",
+  }
+});
+```
+
+## Agent Failure Taxonomy
+
+```yaml
+agent_failures:
+  planning: [wrong_plan, incomplete_plan]
+  tool_selection: [wrong_tool, missed_tool, unnecessary_call]
+  tool_execution: [wrong_parameters, type_error]
+  state_management: [lost_context, stuck_in_loop]
+  error_recovery: [no_fallback, wrong_fallback]
+```
+
+## Transition Matrix (Agents)
+
+Shows where failures occur between states:
+
+```python
+def build_transition_matrix(conversations, states):
+    matrix = defaultdict(lambda: defaultdict(int))
+    for conv in conversations:
+        if conv["failed"]:
+            last_success = find_last_success(conv)
+            first_failure = find_first_failure(conv)
+            matrix[last_success][first_failure] += 1
+    return pd.DataFrame(matrix).fillna(0)
+```
+
+## Principles
+
+- **MECE** - Each failure fits ONE category
+- **Actionable** - Categories suggest fixes
+- **Bottom-up** - Let categories emerge from data