AI & LLM Skill
Agent Eval Suite Langsmith
Production agent eval suite LangSmith dataset curation + Promptfoo assertion framework +…
$15
LLM Fine-Tuning Pipeline
spesifik LLM uretmek icin uctan uca fine-tuning playbook OpenAI hosted FT (GPT-4o-mini/4.1)…
An end-to-end playbook for producing a customer-specific LLM that holds a consistent brand voice, combining OpenAI hosted fine-tuning with self-hosted Qwen3 LoRA adapters. It walks dataset curation, PII masking, train/eval splitting, a three-metric evaluation suite, and serving with a fallback chain, so a fine-tune is measured and reversible rather than a leap of faith.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category AI & LLM
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
Fine-tuning has to beat the base model by a set margin or the project stops. Fifty excellent examples outrank five hundred mediocre ones, three metrics gate the result, and a fallback chain catches failures in production.
- Measures the base model baseline first with a lexical overlap score plus an independent judge model scoring brand voice; fine-tuning must beat that baseline by a set margin or the project stops here
- Curates 50-200 training examples favoring quality over volume (50 excellent beat 500 mediocre), and masks personal data such as ID numbers, emails and phone numbers with regex before anything is uploaded
- Splits 80/20 into train and hold-out eval, deliberately seeding the eval set with edge cases and adversarial prompts the model must refuse, then keeps the two files strictly separate
- Trains a low-rank adapter on the self-hosted model (about 1% of parameters, roughly 30 minutes on a single GPU) or submits a hosted fine-tune job, whichever the monthly cost math favors
- Gates the result on three metrics at once: lexical overlap above 0.7, brand-voice judge above 4 of 5, adversarial pass rate above 95%; on regression it early-stops and rolls back to the last good checkpoint
- Deploys behind a fallback chain where a failed or timed-out tuned model silently routes to the base model with few-shot prompting, then monitors monthly and retrains as new approved examples accumulate
One power source. 6 lines out.
fine-tuning-pipeline-llm · core
core active · 6 lines
- ✓ converting curated examp…
Converting curated examples into clean JSONL chat-format training data
- ✓ masking pii (id numbers
Masking PII (ID numbers, email, phone) in training data for privacy compliance
- ✓ training a qwen3-7b lora
Training a Qwen3-7B LoRA adapter with PEFT instead of an expensive full fine-tune
- ✓ launching an openai hosted
Launching an OpenAI hosted fine-tuning job and polling it to completion
- ✓ evaluating a fine-tuned
Evaluating a fine-tuned model with ROUGE-L, an LLM-as-judge rubric and adversarial checks
- ✓ serving fine-tuned models
Serving fine-tuned models with vLLM adapter swap and a few-shot fallback chain
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Lock in a consistent brand voice that a model preserves across every generated report
license: perpetual -
Cut inference cost and prompt size by moving few-shot examples into a trained adapter
license: perpetual -
Catch overfitting and regressions before deploy via held-out eval and three metrics
license: perpetual -
Keep service reliable with a fallback to a base model and few-shot when the fine-tune fails
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
Dataset curation script that converts source examples to JSONL with PII masking regexes
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
ML and platform engineers who need a measured, cost-aware way to produce brand-consistent custom models instead of fine-tuning on faith.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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Do I need my own GPUs, or can I avoid self-hosting entirely?
Both paths are covered, so you can use OpenAI hosted fine-tuning with no GPUs, or run a self-hosted Qwen3 LoRA adapter when you want control and lower per-call cost. You choose based on budget and how much you need to own the model.
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Can't I just use prompting or RAG instead of fine-tuning?
Often you can, and the playbook is measured rather than fine-tune-first, with an evaluation suite to prove it earns its keep. Fine-tuning is for when prompting plateaus on consistent voice, not a reflex.
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Will fine-tuning teach the model new facts about my business?
No, this targets voice and style consistency, not knowledge. For facts and current data you want retrieval (see embedding-strategies), because fine-tuning bakes in tone, not a source of truth.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.