Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.
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Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.
Format production logs into SFT training data (API logs, user corrections, accept/reject signals)
Distill from frontier models using batch APIs (OpenAI, Anthropic) to label production inputs
Bootstrap synthetic data when fewer than 1000 real examples exist
Validate data quality before training (dedup, schema check, diversity metrics)
Split data into train/eval sets with production data reserved for evaluation
Three Data Paths
Path
When to Use
Data Source
Cost
A) Production data
Have API logs or user feedback
Your own production systems
Free (already collected)
B) Frontier distillation
Have production inputs but no labels
OpenAI/Anthropic batch APIs
~50% of real-time API cost
C) Synthetic bootstrap
< 1000 real examples
Frontier model generation
Varies by volume
Always prefer Path A — production data is the moat competitors can't replicate.
JSONL Chat Format
All training platforms (Tinker, Unsloth, TRL, Axolotl) accept this standard chat format:
{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is 2+2?"}, {"role": "assistant", "content": "4"}]}
{"messages": [{"role": "user", "content": "Translate to French: Hello"}, {"role": "assistant", "content": "Bonjour"}]}
Format Rules
One JSON object per line, no trailing commas
messages array with role and content fields
Roles: system (optional, first only), user, assistant (alternating)
Multi-turn: alternate user/assistant pairs within a single messages array
UTF-8 encoding, no BOM
assistant messages are the training targets — everything else is context
Platform-Specific Notes
Tinker: Standard chat format above. Max 32K tokens per example. System message optional.
Unsloth/TRL: Same format. Also accepts {"prompt": "...", "completion": "..."} for simple pairs. Chat format preferred for multi-turn.
Axolotl: Supports multiple formats via config. Recommend chat_template type with standard JSONL.
Path A: Production Data Collection
From API Logs
If you log API requests/responses, convert them directly:
import json
defapi_log_to_training(log_entry):
"""Convert an API request/response log to training format."""
messages = []
# Add system prompt if presentif log_entry.get("system_prompt"):
messages.append({
"role": "system",
"content": log_entry["system_prompt"]
})
# Add the user's input
messages.append({
"role": "user",
"content": log_entry["user_input"]
})
# Add the response (use corrected version if available)
response = log_entry.get("corrected_response") or log_entry["api_response"]
messages.append({
"role": "assistant",
"content": response
})
return {"messages": messages}
# Process logswithopen("api_logs.jsonl") as f, open("training_data.jsonl", "w") as out:
for line in f:
log = json.loads(line)
example = api_log_to_training(log)
out.write(json.dumps(example) + "\n")
From User Corrections
User corrections (edits to model output) are the highest-quality training signal:
defcorrection_to_training(original_input, corrected_output, system_prompt=None):
"""Convert a user correction into a training example.
The corrected output becomes the training target."""
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": original_input})
messages.append({"role": "assistant", "content": corrected_output})
return {"messages": messages}
From Accept/Reject Signals
If users accept or reject model outputs, use accepted outputs as positive examples:
deffilter_accepted(logs):
"""Keep only examples where user accepted the output."""
accepted = []
for log in logs:
if log.get("user_action") == "accepted":
accepted.append({
"messages": [
{"role": "user", "content": log["input"]},
{"role": "assistant", "content": log["output"]}
]
})
return accepted
Path B: Frontier Distillation
Use frontier models to label your production inputs. Best when you have real inputs but no gold labels.
OpenAI Batch API (50% discount)
import json
defcreate_batch_file(inputs, system_prompt, model="gpt-4o"):
"""Create a batch file for OpenAI Batch API."""
requests = []
for i, user_input inenumerate(inputs):
requests.append({
"custom_id": f"request-{i}",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input}
],
"max_tokens": 4096
}
})
withopen("batch_input.jsonl", "w") as f:
for req in requests:
f.write(json.dumps(req) + "\n")
return"batch_input.jsonl"# Submit batch# openai api batches create -i batch_input.jsonl -e /v1/chat/completions -c 24h
defbatch_results_to_training(results_file, inputs, system_prompt=None):
"""Convert batch API results into training JSONL."""
training = []
withopen(results_file) as f:
for line in f:
result = json.loads(line)
idx = int(result["custom_id"].split("-")[1])
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": inputs[idx]})
# Extract assistant response from batch result
content = result["response"]["body"]["choices"][0]["message"]["content"]
messages.append({"role": "assistant", "content": content})
training.append({"messages": messages})
withopen("distilled_training.jsonl", "w") as f:
for example in training:
f.write(json.dumps(example) + "\n")
returnlen(training)
Path C: Synthetic Bootstrapping
Generate training data from scratch when you have < 1000 real examples. Use as a starting point, then replace with production data as it accumulates.
Seed Prompt Strategy
import openai
client = openai.OpenAI()
defgenerate_synthetic_examples(task_description, seed_examples, n=500, model="gpt-4o"):
"""Generate diverse synthetic training examples from seed examples."""
meta_prompt = f"""You are generating training data for an LLM that will be fine-tuned for:
{task_description}
Here are {len(seed_examples)} real examples of the desired behavior:
{json.dumps(seed_examples[:5], indent=2)}
Generate a NEW, diverse example. The input should cover a different scenario than
the seeds. The output should match the quality and style of the examples above.
Return JSON: {{"input": "...", "output": "..."}}"""
examples = []
for i inrange(n):
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": meta_prompt}],
response_format={"type": "json_object"},
temperature=0.9, # High temp for diversity
)
example = json.loads(response.choices[0].message.content)
examples.append({
"messages": [
{"role": "user", "content": example["input"]},
{"role": "assistant", "content": example["output"]}
]
})
return examples
Diversity Strategies
Vary temperature (0.7-1.0) across generation batches
Use different frontier models (GPT-4o, Claude, Gemini) to reduce model-specific bias
Seed with representative prompts from different categories/difficulty levels
Include edge cases and adversarial examples explicitly in seed prompts
Data Quality Validation
Schema Validation
defvalidate_jsonl(filepath):
"""Validate JSONL training file format."""
errors = []
valid = 0withopen(filepath) as f:
for i, line inenumerate(f, 1):
try:
obj = json.loads(line)
except json.JSONDecodeError as e:
errors.append(f"Line {i}: Invalid JSON — {e}")
continueif"messages"notin obj:
errors.append(f"Line {i}: Missing 'messages' key")
continue
msgs = obj["messages"]
ifnotisinstance(msgs, list) orlen(msgs) < 2:
errors.append(f"Line {i}: 'messages' must be a list with >= 2 entries")
continue# Check roles
has_user = any(m.get("role") == "user"for m in msgs)
has_assistant = any(m.get("role") == "assistant"for m in msgs)
ifnot has_user ornot has_assistant:
errors.append(f"Line {i}: Must have at least one user and one assistant message")
continuefor j, msg inenumerate(msgs):
if"role"notin msg or"content"notin msg:
errors.append(f"Line {i}, message {j}: Missing 'role' or 'content'")
elif msg["role"] notin ("system", "user", "assistant"):
errors.append(f"Line {i}, message {j}: Invalid role '{msg['role']}'")
valid += 1return {"valid": valid, "errors": errors, "total": valid + len(errors)}
Deduplication (MinHash)
from datasketch import MinHash, MinHashLSH
defdeduplicate_dataset(examples, threshold=0.8):
"""Remove near-duplicate examples using MinHash LSH."""
lsh = MinHashLSH(threshold=threshold, num_perm=128)
unique = []
for i, ex inenumerate(examples):
# Hash the assistant's response (training target)
text = ex["messages"][-1]["content"]
m = MinHash(num_perm=128)
for word in text.lower().split():
m.update(word.encode("utf-8"))
key = f"doc-{i}"ifnot lsh.query(m):
lsh.insert(key, m)
unique.append(ex)
removed = len(examples) - len(unique)
print(f"Removed {removed} duplicates ({removed/len(examples)*100:.1f}%)")
return unique
Diversity Metrics
from collections import Counter
defdistinct_n(texts, n=2):
"""Calculate distinct-n metric (ratio of unique n-grams to total n-grams)."""
total_ngrams = Counter()
for text in texts:
words = text.lower().split()
ngrams = [tuple(words[i:i+n]) for i inrange(len(words)-n+1)]
total_ngrams.update(ngrams)
ifsum(total_ngrams.values()) == 0:
return0returnlen(total_ngrams) / sum(total_ngrams.values())
defdataset_diversity_report(examples):
"""Generate diversity metrics for a training dataset."""
responses = [ex["messages"][-1]["content"] for ex in examples]
inputs = [m["content"] for ex in examples for m in ex["messages"] if m["role"] == "user"]
report = {
"total_examples": len(examples),
"avg_response_length": sum(len(r.split()) for r in responses) / len(responses),
"avg_input_length": sum(len(i.split()) for i in inputs) / len(inputs),
"distinct_1": distinct_n(responses, 1),
"distinct_2": distinct_n(responses, 2),
"distinct_3": distinct_n(responses, 3),
}
return report
Train/Eval Split
import random
defsplit_dataset(examples, eval_ratio=0.1, production_indices=None):
"""Split dataset into train/eval, keeping production data in eval for ground truth.
Args:
examples: List of training examples
eval_ratio: Fraction of data for evaluation (default 10%)
production_indices: Indices of real production examples (always go to eval)
"""
production_indices = set(production_indices or [])
synthetic = [ex for i, ex inenumerate(examples) if i notin production_indices]
production = [ex for i, ex inenumerate(examples) if i in production_indices]
# Production data goes to eval (ground truth)
eval_set = list(production)
# Fill remaining eval budget from synthetic
remaining_eval = max(0, int(len(examples) * eval_ratio) - len(eval_set))
random.shuffle(synthetic)
eval_set.extend(synthetic[:remaining_eval])
train_set = synthetic[remaining_eval:]
print(f"Train: {len(train_set)}, Eval: {len(eval_set)} "f"({len(production)} production + {len(eval_set)-len(production)} synthetic)")
return train_set, eval_set
Common Issues
Issue
Cause
Fix
JSONDecodeError
Trailing commas or malformed JSON
Run validate_jsonl() and fix flagged lines
Tokenizer mismatch
Data tokenized for wrong model
Always use target model's tokenizer for length checks
