LLM Block
The LLM Block in GLIK Studio is a node that routes data into a large language model (LLM) — such as OpenAI's GPT or Anthropic's Claude — and returns a generated response. It enables natural language understanding, reasoning, and synthesis to be embedded inside a structured enterprise workflow.
It acts as a dynamic reasoning node for:
Decision explanation
Ambiguity resolution
Freeform synthesis (e.g. summaries, justifications, answers)
🧱 Block vs. Node in GLIK
A block is a configurable building element in GLIK Studio's visual interface.
A node is an instance of that block placed into a specific workflow (graph).
Think of a block as a type or template — and a node as its usage in a particular context.
🌐 GLIK Studio vs. GLIK Cloud
GLIK Studio is the visual builder for composing workflows using blocks (like LLM Block).
GLIK Cloud is the managed runtime environment where workflows are executed — it runs and scales your AI-powered applications.
Workflows designed in Studio are published to Cloud.
💼 Enterprise Use Case Context
The LLM Block allows enterprise builders to embed generative reasoning into business logic. It becomes especially useful in:
For Enterprise Developers
Fallback logic: Handle uncertain or missing cases in rules-based flows.
Complex prompt chains: Inject variables and memory into prompts to drive dynamic reasoning.
Human-like summarization: Explain a decision using natural language.
For Executive Decision Makers
Transparency: LLMs explain how and why an approval was made.
AI as a co-pilot: Use LLMs to evaluate nuanced decisions based on enterprise context.
Policy compliance: Summarize which thresholds, rules, or values were triggered.
🛠 Common Configurations
Prompt Input
The system message or instruction for the LLM
Variables Used
Pulls from upstream nodes or session variables
Model Target
Can be set to a default (e.g., GPT-4, Claude)
Output Binding
Stores output in a variable like conversation_notes
📘 Example Usage
In the Expense Policy Decision Engine, the LLM Block is triggered when invoice data is incomplete or ambiguous. It generates an explanation and decision recommendation such as: "Based on the vendor history and lack of an uploaded policy document, this expense should be escalated for manual review."
🧠 Best Practices
Always use fallback conditions to avoid over-relying on the LLM
Keep prompts deterministic and structured if used in workflows that affect finance, security, or compliance
Include memory injection if context across steps is important
🚀 Looking Ahead
Future versions of GLIK will support:
Fine-tuned models per organization
Prompt versioning and audit logs
Role-based prompt injection for use-case control
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