EXAONE 3.5

Cost & access

EXAONE 3.5 is proprietary via LG AI Research. Typical billing: input + output tokens; ChatGPT-style subscriptions are separate from API access. With a 128K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Korean bilingual frontier model.
• Modalities: text · License: Proprietary · Released 2024-12-05.
• Best-fit workflows for this model:
• • Drafting, summarization, and structured extraction from long documents.

Technical foundation

• LG AI Research reports Undisclosed parameters; training data: Undisclosed.
• Context: 128K tokens. Open weights: no.
• EXAONE 3.5 is positioned as a general-purpose model in the LG AI Research lineup.

First API call

Follow LG AI Research's official SDK for EXAONE 3.5; use model id "exaone-3-5" from their docs.

# See https://example.com/exaone-3-5
# Model id: exaone-3-5

Important technical topics

• Prompting EXAONE 3.5: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for LG AI Research billing data."
• Temperature: use 0–0.3 for extraction and compliance on EXAONE 3.5; 0.7–1.0 for brainstorming.
• Tokens: EXAONE 3.5 bills by tokens (~¾ word each). Undisclosed parameters affect capability; your bill is driven by context length and call volume.
• Context window (128K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for EXAONE 3.5.

Real enterprise patterns

• RAG with EXAONE 3.5: retrieve from your vector DB, cite sources in the prompt.
• Tool calling: define JSON schemas; let EXAONE 3.5 request functions, not free-form SQL.
• Eval suite: regression prompts before each model or prompt change.
• Cost routing: default to EXAONE 3.5 for hard tasks; smaller sibling model for triage.

Production & security

• Secrets: never commit keys for EXAONE 3.5; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=exaone-3-5, tokens in/out, latency.
• Rate limits: handle LG AI Research 429/5xx with exponential backoff and circuit breakers.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Support copilot: EXAONE 3.5 drafts replies from KB snippets.
• Contract clause extractor with human approval.
• Weekly metrics narrative from SQL + CSV exports.
• Agent that files expenses from receipt photos (if multimodal).

Suggested stack

• Language: Python 3.11+
• LLM: EXAONE 3.5 (LG AI Research official SDK)
• UI: Streamlit or Next.js for internal tools
• APIs: FastAPI
• Vector DB (RAG): Pinecone / Chroma / pgvector

Learning path

• Python basics
• HTTP/REST and environment variables
• LG AI Research authentication and EXAONE 3.5 model id (exaone-3-5)
• First successful call to EXAONE 3.5
• Prompt design and JSON / structured outputs
• RAG
• Tool use / function calling
• Evals and regression sets
• Production deploy + monitoring