docs(examples): add beginner-friendly LLM tracing example

examples/llm_tracing_101.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+"""
+LLM Tracing 101 — A beginner-friendly guide to LLM observability with Traceloop.
+
+This example shows how to:
+1. Initialize Traceloop
+2. Make an LLM call with OpenAI
+3. See the trace output in your console (no backend needed!)
+
+Setup:
+    pip install openai opentelemetry-sdk opentelemetry-api traceloop-sdk
+    export OPENAI_API_KEY="sk-..."
+
+Run:
+    python llm_tracing_101.py
+"""
+
+import os
+from opentelemetry.sdk.trace.export import ConsoleSpanExporter
+from traceloop.sdk import Traceloop
+
+
+def main():
+    # ------------------------------------------------------------------
+    # Step 1: Initialize Traceloop with the ConsoleSpanExporter
+    # ------------------------------------------------------------------
+    # The ConsoleSpanExporter prints traces to your terminal, so you can
+    # see exactly what Traceloop captures without configuring any
+    # external observability backend (like Jaeger, Datadog, or Honeycomb).
+    Traceloop.init(
+        app_name="llm-tracing-101",
+        exporter=ConsoleSpanExporter(),
+    )
+
+    # ------------------------------------------------------------------
+    # Step 2: Make an LLM call
+    # ------------------------------------------------------------------
+    # Traceloop auto-instruments OpenAI, so every call you make will be
+    # traced automatically. No decorators or context managers needed.
+    from openai import OpenAI
+
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+    print("Making LLM call...")
+
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What is observability in 2 sentences?"},
+        ],
+        max_tokens=100,
+    )
+
+    print(f"\nResponse: {response.choices[0].message.content}")
+
+    # ------------------------------------------------------------------
+    # Step 3: View the trace
+    # ------------------------------------------------------------------
+    # After the script finishes, you'll see a JSON trace printed to your
+    # terminal. Look for:
+    #   - "name": "openai.chat" — the LLM call span
+    #   - "attributes" — model, prompt tokens, completion tokens, etc.
+    #
+    # In production, swap ConsoleSpanExporter for an OTLP exporter to
+    # send traces to your observability backend of choice.
+
+
+if __name__ == "__main__":
+    main()