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
In Progress
This page is currently under active development. Content may be incomplete, evolving, or placeholder-only. Please check back later for finalized documentation and fully structured examples.
🚀 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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