Production-Ready Code with Snipara + RLM-Runtime: Eliminate AI Hallucinations

Your AI-generated code compiles. It even looks reasonable. But when it hits production, things break. The function signatures don't match your codebase. The patterns contradict your team's standards. The edge cases were never considered. This isn't a model problem—it's a context and verification problem. Here's how combining Snipara's context optimization with RLM-Runtime's sandboxed execution creates code that actually works.

The Hallucination Problem in AI-Generated Code

Every developer using AI coding assistants has experienced this: the code looks correct, the syntax is valid, but something is subtly wrong.

Common Hallucination Patterns

The root cause is simple: the LLM doesn't know your codebase. It knows patterns from millions of repositories, but not the specific function you wrote last week.

Feeding your entire codebase as context doesn't solve this—it creates new problems:

• 500K tokens of noise drowns out the signal
• $1.50+ per query burns through your API budget
• Slower responses as the model processes irrelevant code
• Context window limits force arbitrary truncation

The Two-Part Solution

Production-ready AI code requires two things that are rarely combined:

The Quality Loop

How Snipara Reduces Hallucinations

Snipara isn't RAG. It's context engineering—a fundamentally different approach to giving LLMs the information they need.

Hybrid Search vs. Pure Vector Search

Traditional RAG uses vector embeddings to find “semantically similar” content. This fails for code because:

• Searching “authentication” might return your AuthService class
• But it won't find validateJWT() because the name isn't semantically similar
• The LLM then hallucinates a function that doesn't exist

Snipara's hybrid approach:

What you query

rlm_context_query("implement login endpoint authentication")

What Snipara returns (ranked by relevance)

AuthService.ts:45-89     # Exact match: "authentication"

validateJWT.ts:12-34     # Keyword match: "JWT" in auth context

middleware/auth.ts:1-50  # Semantic match: auth patterns

CODING_STANDARDS.md      # Shared context: team patterns

The result: the LLM sees actual function signatures, not guessed ones.

Team Standards via Shared Context

Every team has unwritten rules:

• “We use Zod for all API validation”
• “Database queries go through the repository pattern”
• “Error responses follow RFC 7807 format”

Snipara's shared context collections inject these rules into every query automatically. The LLM doesn't hallucinate patterns—it follows yours.

How RLM-Runtime Validates Code

Context optimization gets you 80% of the way. The remaining 20% is validation: does this code actually run?

Docker vs. Local Execution

The Execution Loop

RLM-Runtime doesn't just run code once—it iterates until success:

from rlm import RLM

rlm = RLM(

    backend="anthropic",

    environment="docker",

    max_depth=5,  # Maximum iteration attempts

    snipara_api_key="rlm_...",

    snipara_project_slug="my-project"

)

result = rlm.completion("""

    Implement the /api/users/register endpoint.

    Write tests and run them.

    Only return when ALL tests pass.

""")

Real Example: Implementing OAuth Login

Let's walk through adding GitHub OAuth to an existing API.

Step 1: Query Context

from rlm import RLM

rlm = RLM(

    backend="anthropic",

    environment="docker",

    snipara_api_key="rlm_...",

    snipara_project_slug="my-saas-api"

)

Snipara automatically returns:

- src/auth/providers/google.ts (existing template)

- src/auth/session.ts (session patterns)

- CODING_STANDARDS.md (OAuth must use PKCE flow)

Step 2: Generate and Validate

result = rlm.completion("""

    Add GitHub OAuth provider following the existing

    Google OAuth pattern.

    Context from Snipara shows:

    - Use PKCE flow (MANDATORY per coding standards)

    - Follow existing google.ts structure

    - Use createOrUpdateUser from user repository

    Tasks:

    1. Create src/auth/providers/github.ts

    2. Write integration tests

    3. Run tests in Docker

    4. Verify all pass before returning

""")

Why the Generated Code Is Production-Ready

Measuring Quality Improvement

The key insight: It's not about perfect code on the first try. It's about fast iteration with real feedback. Docker execution provides that feedback in seconds, not after deployment.

Getting Started

Installation

pip install rlm-runtime[all]

docker --version  # Verify Docker is running

rlm init

rlm run --env docker "print('Hello from Docker')"

Connect Snipara

from rlm import RLM

rlm = RLM(

    backend="anthropic",  # or "openai", "litellm"

    environment="docker",

    snipara_api_key="rlm_your_key_here",

    snipara_project_slug="your-project-slug",

    max_depth=5,

    verbose=True,  # See execution logs

)

When to Use This Workflow

Conclusion

AI code generation is powerful, but raw LLM output isn't production-ready. The solution isn't a smarter model—it's a smarter workflow:

1. Snipara gives the LLM your actual patterns, not guesses
2. RLM-Runtime validates code works before it leaves the sandbox
3. Iteration catches what the first pass missed

The result: code that follows your standards, calls your real functions, and passes your tests—before a human ever reviews it.