RAG-Powered Continuous Learning

AI that learns from your team — every edit, every failure, every document makes tests smarter.

What It Does

RAG-Powered Continuous Learning transforms Shift-Left API from a static test generator into an intelligent system that improves over time. When generating new tests, the platform retrieves similar past test cases, historical failure patterns, extracted business rules, and user corrections from a vector store — then injects this context into the AI prompt. The result: each new generation is informed by your team's accumulated knowledge. Upload requirement documents (PDF, Word, Excel, HTML) and the AI automatically extracts atomic business rules with confidence scoring. These rules are then enforced in every future test generation. When tests fail or users make corrections, the learning engine synthesizes patterns (e.g., "always include content-type assertions for JSON endpoints") and feeds them back into the generation pipeline.

RAG-powered requirements ingestion for AI-driven test generation from business documents

Overview

Shift-Left API uses Retrieval-Augmented Generation (RAG) to continuously improve test quality. The platform embeds test cases, execution failures, and domain rules into a vector store, then retrieves the most relevant context when generating new tests. It learns from every user edit, every test failure, and every document you upload — creating a feedback loop that makes each generation smarter than the last. Upload business requirement documents (PDF, Word, Excel) and the AI extracts rules that are automatically enforced in future test generation.

Key Capabilities

Vector-based similarity search retrieves relevant past tests and failure patterns
Learns from user edits — tracks added assertions, deleted tests, and field corrections
Analyzes execution failures to avoid repeating the same mistakes
Upload requirement documents (PDF, DOCX, XLSX, CSV) for AI-powered rule extraction
Multi-provider embeddings: OpenAI, Google Gemini, Cohere, or local
Auto-regenerates low-quality tests when pass rate drops below thresholds
Exports high-quality generations as training data for fine-tuning

How It Works

  1. 1

    Every test case, user edit, and failure is embedded into a vector store automatically

  2. 2

    When generating new tests, RAG retrieves the top 8 most relevant context items by similarity

  3. 3

    Results are re-ranked by quality signals: pass rate, user edit count, and relevance score

  4. 4

    Upload business documents — AI extracts rules with anti-hallucination validation

  5. 5

    The learning engine synthesizes user correction patterns into prompt guidance

  6. 6

    Low-quality tests are auto-flagged for regeneration (pass rate < 50%, quality < 60)

Available on

All Plans

Included in the free trial — no credit card required.

Included in all plans
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