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LLMs
72 posts about LLMs. Every summary links to the original.
Grab ·
From failure to success: The birth of GrabGPT, Grab’s internal ChatGPT
Grab's machine learning platform team initially faced overwhelming volumes of repetitive user inquiries across their internal support channels. An initial attempt to automate answers using the open-source chatbot-ui framework and GPT-3.5-turbo failed to scale because the 8,000-token context limit could not accommodate extensive documentation, and embedding search proved inadequate. The project then pivoted to create an internal conversational AI platform called GrabGPT by wiring chatbot-ui with Google authentication and Grab's catwalk model-serving infrastructure. The resulting internal service rapidly expanded across the organization, providing auditable interactions, multi-model support across OpenAI, Claude, and Gemini, and private network routing to safeguard corporate data.
Wenbo WeiGrab ·
Streamlining RiskOps with the SOP agent framework
Manual Account Takeover (ATO) investigations in Risk Operations traditionally demand intensive cross-referencing across systems, manual SQL execution, and high-pressure decision-making prone to human error. To resolve these bottlenecks, an SOP-driven LLM agent framework models investigative workflows as natural-language tree structures with explicit function notations like @function_name. Execution is coordinated between an SOP planner, which traverses the tree using a Depth-First Search strategy, and a Worker Agent that parses JSON-formatted steps to invoke database queries and APIs. Once all steps evaluate their decision criteria, the framework synthesizes the collected data into an actionable summary report. Implementing this architecture automated 87% of ATO cases and dropped average ticket handling time from 22 minutes to 3 minutes.
Fujiao LiuGrab ·
Introducing the SOP-driven LLM agent frameworks
Standard operating procedure (SOP) driven Large Language Model agent frameworks address common generative AI challenges such as hallucinations, non-standard output formats, and branching navigation errors. Workflows are represented as hierarchical trees where nodes encapsulate actions or decision points that can be created using a visual editor and annotated with explicit external function calls. Execution relies on a tripartite architecture consisting of a Depth-First Search planner module with backtracking, an adaptive worker agent that limits API exposure and compresses context, and a multilingual user agent. Supporting tools include a Graph Retrieval-Augmented Generation pipeline, a plugin system integrating Python and SQL, and a state stack for pausing workflows during human intervention. In production deployments for fraud and account takeover investigations, the framework automated up to 87% of cases while cutting handling times substantially.
Fujiao LiuGrab ·
Grab AI Gateway: Connecting Grabbers to multiple GenAI providers
Grab built the AI Gateway to centralize access, cost control, and security across external and open-source Generative AI providers such as OpenAI, Azure, AWS, and Google. Designed as a set of lightweight reverse proxies, the gateway manages authentication, rate limiting, and authorization while translating payloads into a unified OpenAI-compatible interface. The platform archives request metadata and calculated per-call costs into a central data lake for auditing and showback, dynamically routing traffic across shared reserved capacity and regions to mitigate quota throttling. Supporting over 300 internal use cases, the system integrates directly with internal development notebooks and deployment tools to power applications ranging from real-time audio safety analysis to automated content moderation.
Bjorn JeeGrab ·
Supercharging LLM application development with LLM-Kit
Grab developed LLM-Kit to resolve standardization, security, observability, and infrastructure provisioning bottlenecks across its generative AI development efforts. Submitting an initial application request automatically triggers project generation in GitLab, outputting a modular codebase along with Terraform definitions for Amazon EKS and ECR. The scaffolded architecture incorporates FastAPI, LangChain, OpenID Connect authentication helpers, and PGVector alongside HashiCorp Vault for credential management. Observability and quality tracking are built in through direct integrations with Datadog and LangSmith evaluations. By providing these standardized paved-road templates, the framework has onboarded hundreds of generative AI applications and saved teams an estimated 1.5 weeks of initial setup time.
Boon Zhan ChewGrab ·
Metasense V2: Enhancing, improving and productionisation of LLM powered data governance
Grab scaled Metasense to automate metadata generation, column-level classification, and sensitivity tiering across its entire data lake. Post-rollout analysis revealed that high tag volumes, wide tables, and mixed content—such as nested JSON and customer communications—strained model capacity and led to missed Personally Identifiable Information (PII). To overcome these limitations, the team split the classification process into distinct PII and non-PII tasks, reduced prompt word counts, and partitioned tables with over 150 columns into smaller units. Integrating LangChain and LangSmith modernized the architecture by enabling direct prompt experimentation, custom metric tracking, and version-controlled deployments. The updated pipeline achieved low misclassification rates alongside automated alert thresholds to trigger model improvement protocols if errors increase.
Nick BuhrerGrab ·
LLM-assisted vector similarity search
Vector similarity search often struggles with conceptual nuances, negations, and complex constraints despite its efficiency in high-dimensional semantic retrieval. To address this limitation, a two-step retrieval pipeline combines Facebook AI Similarity Search (FAISS) using OpenAI's text-embedding-ada-002 embeddings with a large language model re-ranking step powered by GPT-4o. The initial FAISS search extracts a candidate shortlist of matches, which GPT-4o then re-ranks based on contextual relevance and query intent. Evaluations on structured datasets showed that while simple queries yielded comparable results across methods, the LLM-assisted approach effectively resolved negations and conceptual matching where raw vector search failed. Internal production trials on relational datasets exceeding 4,500 rows demonstrated similar relevance improvements, with additional query latency being the primary operational trade-off.
Md RiyadhGrab ·
Leveraging RAG-powered LLMs for analytical tasks
Data analysts at Grab faced an increasing volume of stakeholder data requests that required manually writing and executing repetitive SQL queries with minor parameter changes. To automate routine tasks like metric reporting and fraud investigations, the Integrity Analytics team paired an internal prompt-management platform, Spellvault, with Data-Arks, an in-house Python-based middleware that packages SQL queries and Python functions into APIs. A scheduler triggers automated report summarization, while an analytical bot named A* bot executes relevant investigation queries via RAG and returns summarized findings in Slack. Automated report generation saves an estimated three to four hours per report and shortens multi-query fraud investigations to minutes. Grab favored RAG over fine-tuning because it avoids continuous retraining costs, scales faster across diverse use cases, and surfaces current production data.
Edmund HongGrab ·
Enabling conversational data discovery with LLMs at Grab
Grab faced major bottlenecks in dataset discovery across its 200,000 data lake tables due to low documentation coverage and vanilla Elasticsearch limitations in its internal tool, Hubble. To resolve this, engineers first tuned Elasticsearch by boosting frequently queried P80 tables and certified schemas while deboosting deprecated datasets. They then deployed a GPT-4 pipeline that generated documentation from table schemas and sample data, raising P80 documentation coverage from 20% to roughly 90%. Finally, the team developed HubbleIQ, an LLM-powered assistant built via Glean Apps and integrated into Slack and Hubble search to answer semantic queries. These initiatives increased search click-through rates to 94% and lifted the proportion of users finding discovery easy to 73%.
Shreyas ParbatGrab ·
Chimera Sandbox: A scalable experimentation and development platform for Notebook services
Grab's Chimera team built Chimera Sandbox to address resource accessibility challenges and enable rapid machine learning experimentation across technical and non-technical teams. The platform runs on multi-cluster Kubernetes infrastructure capable of scaling to thousands of nodes per cluster during peak loads. Users can provision customizable Docker environments with varying CPU and GPU configurations, collaborate in real time, and query data using Spark-on-Kubernetes integrations and helper magic cells. The workspace integrates with Grab's AI Gateway for LLM access alongside tools like MLflow, Optuna, RStudio, and LabelStudio on shared storage, backed by user-level cost attribution.
Arkar Min AungGrab ·
How we improved translation experience with cost efficiency
Grab faced translation quality and cost challenges in its booking chat system when tourism resumed after COVID restrictions eased. Device language settings proved unreliable for determining message language, prompting the team to use Lingua alongside booking context heuristics and third-party fallbacks for real-time detection. To replace expensive and inaccurate general translation APIs, Grab distilled a large open-source Hugging Face model into lightweight, language-specific models trained on ten million synthetic chat examples. A post-translation validation step verifies non-translatable entities such as numbers and emojis before falling back to external services if mutations occur. Caching layers were also introduced across translation paths to curb redundant on-the-fly execution.
Jie ZhangGrab ·
LLM-powered data classification for data entities at scale
Grab needed to classify sensitive data at the table and column level across petabytes of database tables and streaming schemas. Manual schema-tiering campaigns had resulted in half of all schemas receiving overly strict Tier 1 access controls, while an initial automated service using regex patterns and third-party machine learning produced high false-positive rates and lacked customizability. To address this, the Caspian data engineering and governance teams enhanced their internal orchestration service, Gemini, by integrating GPT-3.5 via Azure OpenAI. Gemini aggregates classification requests into mini-batches, handles API rate limits, and uses prompt engineering—including few-shot examples, curated tag libraries, and explicit JSON DTO schemas—to reliably tag columns for data owner verification.
Hualin Liu