How do I handle edge cases in my content generation pipeline?

Hey folks, I've been deep in the weeds building out a content generation pipeline for our technical blog, and let me tell you — the happy path is beautiful, but those edge cases will sneak up and bite you! 😅 I started with a simple "LLM → human review → publish" flow, but reality quickly threw in malformed JSON, unexpected nulls, hallucinated citations, and style guide drift.

My core realization? You can't just handle errors reactively; you need to design your pipeline stages to be *resilient* and to *validate* at every step. It's a lot like managing data quality in a complex ETL process. You need checkpoints.

Here’s how I've structured my pipeline now, with specific guardrails:

**1. Input Sanitization & Validation**
Before anything creative happens, I clean and check the input prompts or data seeds. This prevents a lot of downstream weirdness.
```python
# Example: Validating a batch of topic keywords
def validate_topic_batch(topic_list):
validated = []
for topic in topic_list:
if isinstance(topic, str) and 1 < len(topic.strip()) < 50:
# Basic normalization
clean_topic = topic.strip().lower()
if clean_topic not in BLACKLISTED_TOPICS: # Your own list
validated.append(clean_topic)
if not validated:
raise ValueError("Batch resulted in zero valid topics")
return validated
```

**2. Structured Output Parsing with Fallbacks**
I absolutely enforce structured output (like JSON) from the LLM step, but you *must* plan for parsing failures.
- I use a `try/except` wrapper around `json.loads()`
- On failure, I run a small, focused "repair" prompt to fix the JSON.
- If that fails twice, the item goes to a `quarantine_queue` for manual inspection. Don't let one broken item stop your whole batch!

**3. Content-Specific Rule Checks**
After generation, I run automated checks against our style rules. This is my "linting" stage. Think of it like a database constraint.
- **Fact Check List:** For any technical claim (e.g., "Postgres uses MVCC"), the pipeline flags it for cross-reference with a trusted source.
- **Code Block Validation:** Any code block in a specified language (SQL, Python) is run through a basic syntax checker.
- **Tone & Jargon:** A small classifier (or keyword list) checks for overly marketing or complex language we want to avoid.

**4. The Human Review "Smart Queue"**
Not all items need the same level of human attention. I've categorized edge cases into priority queues:
- **P1: Critical.** Failed validation, sensitive topics, controversial claims. Human *must* review.
- **P2: Needs Tweaks.** Minor style deviations, optional fact checks. Can be batch-reviewed.
- **P3: Clean.** Only needs a quick spot-check before publishing.

**5. State Tracking & Idempotency**
This is crucial! Your pipeline will fail midway. Use a state field (like `status`) for each content piece, stored in a simple table. It lets you retry from the point of failure without duplicating work or sending weird "half-baked" content forward.
```sql
-- Simple tracking table
CREATE TABLE content_pipeline (
id UUID PRIMARY KEY,
input_data JSONB,
raw_output TEXT,
validated_output TEXT,
status VARCHAR(50) -- 'raw_generated', 'validation_failed', 'awaiting_review', 'published'
error_log TEXT,
created_at TIMESTAMP
);
```

The biggest lesson? Treat your content pipeline like a data pipeline. Build in idempotent steps, validate at each stage, and have clear fallback paths for when things inevitably go sideways. It saves so much last-minute panic.

What edge cases are you all struggling with? Have you found good tools for the validation layer? I'm always looking to steal—ahem, *learn*—better practices!

—B