AI Code Refactoring: Automation, Safety, Speed

A legacy Python project with 40% code duplication and no type hints — a familiar sight. A senior developer knows it needs fixing, but 80% of the time goes into mechanical rewriting. We take over that mechanical work, leaving architectural decisions and review to the human. Our experience shows: AI refactoring is 5–10 times faster than manual, and with a test safety net, it's safer. Order a trial refactoring of one file — get the result in 1 day and see the quality for yourself.

A typical legacy Python project suffers from three issues. First, lack of type annotations. Second, functions 200 lines long. Third, 40% code duplication. A senior developer spends weeks on manual fixes, yet the result still contains bugs. AI refactoring solves this in days. The cost of refactoring one file is calculated individually.

Why Is AI Refactoring Faster Than Manual?

AI handles the routine in seconds: adds type hints, extracts functions, eliminates duplicates. The human controls the result, not performs mechanical work. This cuts refactoring time from weeks to days. Average budget savings on a project reach 80%.

Types of Refactoring and Approaches

• Structural refactoring (extract method, move class, rename) — well suited to automation, AI accuracy is high.
• Pattern-based refactoring (migrating from callbacks to async/await, adding dependency injection) requires context — AI handles it with the right prompt.
• Architectural refactoring (monolith → microservices, God Object → SRP) — AI generates a plan and draft, final decisions stay with the human.

Refactoring is changing the internal structure of a program without altering its external behavior. We follow this principle, using AI for automation.

How Does the Test Safety Net Guarantee Functionality?

We implemented a safe-refactor system: before changes, all tests are run. If tests fail after refactoring, changes are automatically rolled back. This makes the process safer than manual — human errors are eliminated by 90%. Code review remains with the senior developer.

Async Migration and Type Hints — Examples

def migrate_to_async(source_file: str) -> str:
    """Migrates synchronous code to async/await"""
    source = Path(source_file).read_text()
    response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=8096,
        system="""Migrate Python code from synchronous to async/await.

Rules:
- requests → httpx.AsyncClient
- time.sleep(n) → asyncio.sleep(n)
- threading.Thread → asyncio.create_task
- queue.Queue → asyncio.Queue
- Add async/await to functions that do I/O
- Keep synchronous functions without I/O (pure computation)
- Replace for loops with asyncio.gather where functions are independent""",
        messages=[{"role": "user", "content": f"Migrate to async/await:\n\n```python\n{source}\n```\n\nReturn only the code."}]
    )
    text = response.content[0].text
    if "```python" in text:
        return text.split("```python")[1].split("```")[0].strip()
    return text

def add_type_hints(source_file: str) -> str:
    """Adds type annotations to functions"""
    source = Path(source_file).read_text()
    tree = ast.parse(source)
    unannotated = []
    for node in ast.walk(tree):
        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
            has_annotations = (
                any(arg.annotation for arg in node.args.args) or
                node.returns is not None
            )
            if not has_annotations:
                unannotated.append(node.name)
    if not unannotated:
        return source
    response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=8096,
        messages=[{"role": "user", "content": f"""Add Python type annotations (PEP 484) to functions: {', '.join(unannotated)}

Rules:
- Use from __future__ import annotations for forward references
- For Optional use X | None (Python 3.10+)
- For collections: list[str], dict[str, int], tuple[int, ...]
- For unknown types use Any from typing

```python
{source}

Return the full file with added annotations."""}] ) text = response.content[0].text if "python" in text: return text.split("python")[1].split("```")[0].strip() return text

<details>
<summary>Refactoring example with test safety net</summary>

```python
def safe_refactor(
    source_file: str,
    refactoring_type: str,
    test_file: str = None,
) -> dict:
    """Performs refactoring only if tests pass"""
    source = Path(source_file).read_text()
    refactorer = CodeRefactorer()
    if test_file and Path(test_file).exists():
        result = subprocess.run(
            ["python", "-m", "pytest", test_file, "-v", "--tb=short"],
            capture_output=True, text=True
        )
        if result.returncode != 0:
            return {"success": False, "error": "Tests failing before refactoring", "test_output": result.stdout}
    refactoring = refactorer.refactor(source, refactoring_type)
    backup_file = source_file + ".bak"
    Path(backup_file).write_text(source)
    Path(source_file).write_text(refactoring["refactored"])
    if test_file and Path(test_file).exists():
        result = subprocess.run(
            ["python", "-m", "pytest", test_file, "-v", "--tb=short"],
            capture_output=True, text=True
        )
        if result.returncode != 0:
            Path(source_file).write_text(source)
            return {
                "success": False,
                "error": "Tests failing after refactoring — rolled back",
                "changes": refactoring["changes"],
                "test_output": result.stdout,
            }
    return {
        "success": True,
        "changes": refactoring["changes"],
        "risks": refactoring["risks"],
        "backup": backup_file,
    }

Practical Case: Django Monolith

Situation: Django project, 6 years of development, 45,000 lines. Three problems: no type hints, heavy duplication in view functions, 12 God Object classes.

Applied refactorings (over 3 weeks):

1. add_type_hints for all views.py — automated, 2 hours
2. extract_function for view functions >50 lines — automated + review, 1 week
3. remove_duplication in the service layer — automated, 3 days

Results:

• Type annotations coverage: 12% → 87%
• Average function length: 68 lines → 23 lines
• Code duplication (SonarQube metric): -43%
• New developer onboarding time: 3 weeks → 1.5 weeks

One God Object (OrderService, 1800 lines) required a manual architectural decision — AI suggested 3 decomposition options, developers chose the optimal one.

What's Included

• Codebase analysis and selection of refactoring types
• Prompt development for your specific stack (Python, Django, FastAPI)
• Implementation of safe-refactor with test safety net and backup
• Batch processing of all files (or selective)
• Review of changes by our senior developer
• Documentation of changes and team training
• One month of support after deployment

Process

1. Analysis — scan the codebase, identify problem areas (missing types, long functions, duplication).
2. Design — set priorities, choose refactoring types, configure prompts for your stack.
3. Implementation — run AI refactoring with test safety net, automatically roll back on test failures.
4. Testing — run all tests, check code quality metrics.
5. Deployment — apply changes to the repository, provide a full report.

Estimated Timelines

We guarantee functionality preservation — if tests fail after refactoring, we roll back changes. Contact us to discuss your project: we'll estimate the work in 1 business day. We also provide a certificate of changes made and a metrics report.

Order a project assessment — we'll prepare a detailed plan and calculate the cost.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.