dropboxJun 25, 2026

How we used DSPy to turn AI evaluations into better responses in Dash chat (opens in new tab)

Dropbox uses DSPy to turn AI evaluations into improvements for its Dash chat agent. The process first calibrates LLM judges against human-labeled conversations, then uses those judges to optimize the agent’s system prompt. This feedback loop reduced incomplete answers and token usage while maintaining answer quality. ## The Complexity of Evaluating AI Agents - Agent quality depends on more than the final response: - Understanding user intent - Selecting relevant context - Choosing and using tools - Synthesizing information across documents, messages, and meetings - Handling ambiguity and follow-up turns - Producing grounded, complete answers - Evaluations therefore inspect the full interaction trajectory, not just the output. - Separate evaluations for intent understanding, tool use, context selection, grounding, adaptation, and task completion help identify the source of failures. - Reliable judges were necessary before evaluation results could safely guide agent improvements. ## Calibrating LLM Judges with Human Labels - Dropbox sampled internal chats containing final answers and agent trace logs. - Human reviewers scored five dimensions: - User-intent following - Semantic relevance - Tool calling - Instruction following - Context selection - Reviewers followed a structured process: - Determine whether the agent understood the request. - Check whether it selected appropriate context. - Inspect searches, retrievals, and other tool actions. - Verify that final claims were supported by evidence. - Score relevance, grounding, completeness, and instruction adherence. - Many metrics used a 1–5 scale. - Reviewers also added: - Reasoning notes explaining their scores - Failure codes for issues such as stale evidence, missing context, unsupported claims, incomplete coverage, and poor personalization - These richer annotations helped improve judge prompts while also supporting debugging, error analysis, roadmap planning, and prioritization. ## Using DSPy to Improve Evaluation - DSPy was used to make LLM judges align more closely with human evaluations. - Judges were required to follow a retrospective workflow: - Infer the user’s intent - Inspect the conversation and agent trace - Review supporting evidence - Assess context selection and tool use - Produce scores, failure codes, and reasoning notes - GEPA and MIPROv2, optimization algorithms within DSPy, automatically proposed and tested prompt changes against human-labeled examples. - Optimization supported several scenarios: - Rewriting judge instructions entirely - Adapting a judge to another underlying model - Targeting specific failure modes while preserving the existing evaluation behavior The overall approach creates a scalable improvement loop: human labels calibrate the judges, calibrated judges provide consistent evaluation signals, and those signals guide improvements to the chat agent itself.

llm-as-judgellmagentic-aidspy+4

pinterestJan 13, 2026

PinLanding: Turn Billions of Products into Instant Shopping Collections with Multimodal AI (opens in new tab)

PinLanding is a production pipeline for turning billions of products into searchable shopping collections using multimodal AI. Rather than relying mainly on historical queries or manual curation, it derives structured product attributes from images and metadata, then aligns those attributes with real user search behavior. The system combines multimodal LLMs, embedding-based consolidation, a CLIP-style classifier, and distributed infrastructure to produce scalable, precise shopping feeds. ## Understanding Shopping Intent - Pinterest analyzes search history, autocomplete use, filters, and browsing paths to estimate shopping demand. - Existing systems handle high-volume queries such as “black cocktail dress” well, but provide weaker coverage for: - Long-tail queries - Conversational requests - Contextual intents such as “what to wear for an Italian summer vacation” - The analysis identifies: - Product areas with strong demand but poor collection coverage - Important attribute dimensions, including color, occasion, style, fit, price, and brand - The goal is to expand and improve collection coverage, not replace query understanding. ## Generating and Curating Shopping Topics - Each product is represented by an image plus metadata such as title, description, merchant tags, and price. - A vision-language model generates normalized key-value attributes rather than free-form descriptions. - Raw model output has high recall but produces: - Excessively specific attributes - Near-duplicates such as “boho,” “bohemian,” and “boho-chic” - Sparse attributes that apply to very few products - PinLanding builds a compact vocabulary through: - Frequency filtering to remove rarely useful attributes - Embedding-based clustering to merge semantically similar terms - Manual and LLM-assisted review - An LLM judge evaluates generated topics for semantic coherence, realistic shopping intent, and alignment with natural search phrasing. ## Scalable Attribute Assignment - Running the vision-language model over every product is too expensive and operationally fragile. - PinLanding trains a CLIP-inspired dual encoder: - One encoder embeds product images and text - Another embeds attribute phrases - Matching product-attribute pairs are trained as positives, while mismatches are negatives - A bidirectional contrastive loss aligns related products and attributes. - At inference, products and attributes are embedded once, and attributes are assigned when similarity exceeds a calibrated threshold. - This produces fewer distinct attributes while increasing the average number assigned to each product, creating a denser and more consistent attribute graph. ## Distributed Feed Construction - Ray handles large-scale batch inference across millions of products and topics. - The pipeline separates: - CPU-based image and metadata loading, tokenization, and serialization - GPU-based classifier inference - Streaming allows preprocessing and inference to overlap, while heterogeneous CPU and GPU clusters can scale independently. - The classifier pipeline reportedly completes in about 12 hours using eight NVIDIA A100 GPUs, at an estimated cost of roughly $500 per training run. - Feed construction uses approximate-nearest-neighbor techniques and strict attribute matching. - Topics are represented as attribute tuples, such as: - Category: dress - Color: yellow - Season: summer - Occasion: party - Apache Spark computes topic-product relevance using shared attributes and confidence weights, with partitioning and overlap filters reducing unnecessary candidate comparisons. The core recommendation is to combine user-behavior signals with content-first multimodal modeling. This approach can expand shopping coverage into conversational and long-tail intents while remaining practical through attribute consolidation, contrastive retrieval, and distributed inference.

llm-as-judgemultimodal-aiapache-sparkcontrastive-learning+4