Generative AI Development Services | GenAI Company

Standard SQL databases aren't built for "semantic similarity." A Vector Database (like Pinecone, Milvus, or Weaviate) stores data as high-dimensional embeddings—numerical representations of meaning. When a user asks a question, the system converts that query into a vector and finds the mathematically "closest" data points to provide as context to the LLM.

Standard SQL databases aren't built for "semantic similarity." A Vector Database (like Pinecone, Milvus, or Weaviate) stores data as high-dimensional embeddings—numerical representations of meaning. When a user asks a question, the system converts that query into a vector and finds the mathematically "closest" data points to provide as context to the LLM.

• Grounding: Forcing the model to cite specific sources via RAG.
• Temperature Control: Lowering the temperature parameter (e.g., to 0.1 or 0.2) to make the model more deterministic and less "creative."
• Verification Chains: Implementing a "Self-Check" agent that reviews the output against the retrieved context before displaying it to the user.

It’s more than just asking a question. Technical prompt engineering involves:

• Few-Shot Prompting: Providing 2–3 examples of the desired input/output within the prompt.
• Chain-of-Thought (CoT): Instructing the model to "think step-by-step" to improve reasoning in complex logic or math tasks.
• System Instructions: Defining the model's persona and constraints at the API level (e.g., "You are a senior DevOps engineer; never provide Python code, only Bash scripts.").

We prioritize Data Residency and Anonymization

• Private VPCs: Deploying models within your private cloud (AWS/Azure) so data never leaves your perimeter.
• PII Masking: Using automated pipelines to scrub Personally Identifiable Information (PII) before it reaches the LLM.
• Zero-Data Retention: Utilizing Enterprise API agreements that guarantee your data is not used for future model training.

Traditional GenAI is linear (Input → Output). Agentic workflows allow the AI to use "tools." For example, if an AI agent needs to check a flight status, it doesn't "guess"; it recognizes it needs more info, calls an external API, parses the JSON, and then formulates its response.

Large models (like Llama 3 or GPT-4) are compute-heavy. Quantization reduces the precision of the model's weights (e.g., from 16-bit to 4-bit). This drastically lowers memory usage and speeds up inference with minimal loss in accuracy, allowing high-performance models to run on more affordable hardware.

Standard metrics like "Accuracy" are hard for text. We use:

• BLEU/ROUGE: For comparing generated text to a reference (useful for summarization)
• Faithfulness/Relevancy: Specifically for RAG, measuring if the answer matches the retrieved context.
• Latency & Throughput: Measuring Tokens Per Second (TPS) to ensure the UI feels responsive to users.

• Encoder-only (e.g., BERT): Excellent for understanding relationships and classifying text (Sentiment analysis, Named Entity Recognition).
• Decoder-only (e.g., GPT series): Optimized for generating the next token in a sequence, making them the standard for "Generative" tasks.
• Encoder-Decoder (e.g., T5): Great for translation or summarization where you transform one sequence into another.

Absolutely. At CQLsys, we integrate GenAI into the Software Development Life Cycle by:

• Automated Unit Testing: Generating test cases based on function logic.
• CI/CD Documentation: Automatically updating READMEs and API docs whenever code is pushed.
• Legacy Refactoring: Using LLMs to translate old COBOL or Java code into modern Microservices architectures.